Model Extraction(Stealing) Attacks and Defenses on Machine Learning Models Literature
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updated Sep 25, 2024
This repository contains a curated list of research papers on model extraction attacks and defenses in machine learning, organized by year of publication.
Papers are sorted by their released dates in descending order.
| Year | Title | Venue | Paper Link | Code Link |
|---|---|---|---|---|
| 2024 | A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges | arXiv | Link | |
| 2024 | Graph neural networks: a survey on the links between privacy and security | Artificial Intelligence Review | Link | |
| 2024 | Trustworthy Graph Neural Networks: Aspects, Methods and Trends | Proceedings of the IEEE | Link | |
| 2024 | Safety in Graph Machine Learning: Threats and Safeguards | arXiv | Link | |
| 2024 | SoK: All You Need to Know About On-Device ML Model Extraction - The Gap Between Research and Practice | USENIX | Link | |
| 2024 | Unique Security and Privacy Threats of Large Language Model: A Comprehensive Survey | arXiv | Link | |
| 2023 | A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications | arXiv | Link | |
| 2023 | A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability | arXiv | Link | |
| 2023 | A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly | ScienceDirect | Link | |
| 2023 | I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences | ACM Computing Surveys | Link | |
| 2023 | A Taxonomic Survey of Model Extraction Attacks | IEEE International Conference on Cyber Security and Resilience (CSR) | Link | |
| 2023 | A Survey of Privacy Attacks in Machine Learning | ACM Computing Surveys | Link | |
| 2023 | Adversarial Attack and Defense on Graph Data: A Survey | IEEE Transactions on Knowledge and Data Engineering | Link | |
| 2023 | Model Extraction Attacks Revisited | arXiv | https://arxiv.org/abs/2312.05386 | |
| 2022 | Privacy and Robustness in Federated Learning: Attacks and Defenses | IEEE Transactions on Neural Networks and Learning Systems | Link | |
| 2022 | Towards Security Threats of Deep Learning Systems: A Survey | IEEE Transactions on Software Engineering | Link | |
| 2021 | A Critical Overview of Privacy in Machine Learning | IEEE Security & Privacy | Link | |
| 2021 | When Machine Learning Meets Privacy: A Survey and Outlook | ACM Computing Surveys | Link | |
| 2020 | An Overview of Privacy in Machine Learning | arXiv | Link | |
| 2020 | Privacy-preserving deep learning on machine learning as a service—a comprehensive survey | IEEE Access | Link | |
| 2020 | Privacy and security issues in deep learning: A survey | IEEE Access | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2024 | Special Characters Attack: Toward Scalable Training Data Extraction From Large Language Models | Large Language Models | arXiv | Link | |
| 2024 | LLM-FIN: Large Language Models Fingerprinting Attack on Edge Devices | Large Language Models | ISQED | Link | |
| 2024 | Model Extraction Attack against On-device Deep Learning with Power Side Channel | Deep Neural Networks | ISQED | Link | |
| 2024 | Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2024 | A realistic model extraction attack against graph neural networks | Graph Neural Networks | Knowledge-Based Systems | Link | |
| 2024 | Large Language Models for Link Stealing Attacks Against Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2024 | Link Stealing Attacks Against Inductive Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2024 | Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels | CNN | arXiv | Link | |
| 2024 | SwiftTheft: A Time-Efficient Model Extraction Attack Framework Against Cloud-Based Deep Neural Networks | Deep Neural Networks | Chinese Journal of Electronics | Link | |
| 2024 | Model Extraction Attack Without Natural Images | Deep Neural Networks | ACNS Workshops | ||
| 2024 | DEMISTIFY: Identifying On-device Machine Learning Models Stealing and Reuse Vulnerabilities in Mobile Apps | Mobile ML Models | ICSE | Link | |
| 2024 | Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble Based Sample Selection | Deep Neural Networks | WACV | Link | |
| 2024 | Trained to Leak: Hiding Trojan Side-Channels in Neural Network Weights | Neural Networks | HOST | Link | |
| 2024 | A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning | Split Learning Models | CVPR | Link | |
| 2024 | Prompt Stealing Attacks Against Large Language Models | Large Language Models | arXiv | Link | |
| 2024 | Stealing Part of a Production Language Model | Large Language Models | arXiv | Link | |
| 2024 | Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing | Deep Neural Networks | CVPR | Link | |
| 2024 | Teach LLMs to Phish: Stealing Private Information from Language Models | Large Language Models | arXiv | Link | |
| 2024 | Data-Free Hard-Label Robustness Stealing Attack | Deep Neural Networks | AAAI | Link | |
| 2024 | Large Language Model Watermark Stealing With Mixed Integer Programming | Large Language Models | arXiv | Link | |
| 2024 | PRSA: PRompt Stealing Attacks against Large Language Models | Large Language Models | arXiv | Link | |
| 2024 | QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines | Quantum Neural Networks | arXiv | Link | |
| 2024 | Privacy Backdoors: Stealing Data with Corrupted Pretrained Models | Pretrained Models | arXiv | Link | |
| 2024 | AugSteal: Advancing Model Steal With Data Augmentation in Active Learning Frameworks | Active Learning Models | IEEE TIFS | Link | |
| 2024 | Towards Model Extraction Attacks in GAN-Based Image Translation via Domain Shift Mitigation | GANs | AAAI | Link | |
| 2024 | Stealing Image-to-Image Translation Models With a Single Query | Image-to-Image Translation Models | arXiv | Link | |
| 2024 | A two-stage model extraction attack on GANs with a small collected dataset | GANs | Computers & Security | Link | |
| 2024 | Layer Sequence Extraction of Optimized DNNs Using Side-Channel Information Leaks | Deep Neural Networks | IEEE TCAD | Link | |
| 2024 | COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability | Large Language Models | arXiv | Link | |
| 2024 | Prompt Stealing Attacks Against Text-to-Image Generation Models | Text-to-Image Models | arXiv | Link | |
| 2024 | An Empirical Evaluation of the Data Leakage in Federated Graph Learning | Graph Neural Networks | IEEE Transactions on Network Science and Engineering | Paper | |
| 2024 | AttacKG+:Boosting Attack Knowledge Graph Construction with Large Language Models | Knowledge Graphs | arXiv | Link | |
| 2024 | Adversarial Attacks on Fairness of Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2024 | Unveiling Memorization in Code Models | Code Models | ICSE | Link | |
| 2024 | Unveiling the Secrets without Data: Can Graph Neural Networks Be Exploited through Data-Free Model Extraction Attacks? | Graph Neural Networks | USENIX Security | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2023 | Explanation leaks: Explanation-guided model extraction attacks | Explainable AI Models | Information Sciences | Link | |
| 2023 | Model Leeching: An Extraction Attack Targeting LLMs | Large Language Models | arXiv | Link | |
| 2023 | Are Diffusion Models Vulnerable to Membership Inference Attacks? | Diffusion Models | ICML | Link | |
| 2023 | Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation | Large Language Models | arXiv | Link | |
| 2023 | Model Extraction Attacks on Split Federated Learning | Federated Learning Models | arXiv | Link | |
| 2023 | Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders | Image Encoders | arXiv | Link | |
| 2023 | Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity | Graph Neural Networks | arXiv | Link | |
| 2023 | Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack | Machine Learning Models | NeurIPS | Link | |
| 2023 | D-DAE: Defense-Penetrating Model Extraction Attacks | Deep Neural Networks | IEEE S&P | Link | |
| 2023 | Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models | Image Translation Models | arXiv | Link | |
| 2023 | AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against White-Box Models | White-Box Models | arXiv | Link | |
| 2023 | Efficient Model Extraction by Data Set Stealing, Balancing, and Filtering | Machine Learning Models | IEEE IoT Journal | Link | |
| 2023 | DivTheft: An Ensemble Model Stealing Attack by Divide-and-Conquer | Machine Learning Models | IEEE TDSC | Link | |
| 2023 | Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks | Large Language Models | arXiv | Link | |
| 2023 | A Theoretical Insight into Attack and Defense of Gradient Leakage in Transformer | Transformer Models | arXiv | Link | |
| 2023 | When the Curious Abandon Honesty: Federated Learning Is Not Private | Federated Learning Models | IEEE European Symposium on Security and Privacy (EuroS&P) | Link | |
| 2023 | Making Watermark Survive Model Extraction Attacks in Graph Neural Networks | Graph Neural Networks | IEEE International Conference on Communications | Link | |
| 2023 | A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots | Machine Learning Models | arXiv | Link | |
| 2023 | Private Graph Extraction via Feature Explanations | Graph Neural Networks | Proceedings on Privacy Enhancing Technologies | Link | |
| 2023 | Extracting Privacy-Preserving Subgraphs in Federated Graph Learning using Information Bottleneck | Federated Graph Models | ACM Asia CCS | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2022 | Canary Extraction in Natural Language Understanding Models | NLU Models | arXiv | Link | |
| 2022 | Are Large Pre-Trained Language Models Leaking Your Personal Information? | Large Language Models | arXiv | Link | |
| 2022 | Text Revealer: Private Text Reconstruction via Model Inversion Attacks against Transformers | Transformers | arXiv | Link | |
| 2022 | Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices | Deep Learning Models | ESORICS | ||
| 2022 | DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories | Deep Neural Networks | IEEE S&P | Link | |
| 2022 | DualCF: Efficient Model Extraction Attack from Counterfactual Explanations | Machine Learning Models | FAccT | Link | |
| 2022 | GAME: Generative-Based Adaptive Model Extraction Attack | Machine Learning Models | ESORICS | ||
| 2022 | Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks | Deep Neural Networks | NeurIPS | Link | |
| 2022 | On the Difficulty of Defending Self-Supervised Learning against Model Extraction | Self-Supervised Learning Models | ICML | Link | |
| 2022 | Black-Box Dissector: Towards Erasing-Based Hard-Label Model Stealing Attack | Machine Learning Models | ECCV | ||
| 2022 | Imitated Detectors: Stealing Knowledge of Black-box Object Detectors | Object Detectors | ACM MM | Link | |
| 2022 | StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning | Self-Supervised Learning Models | ACM CCS | Link | |
| 2022 | Model Stealing Attacks Against Vision-Language Models | Vision-Language Models | OpenReview | Link | |
| 2022 | ES Attack: Model Stealing Against Deep Neural Networks Without Data Hurdles | Deep Neural Networks | IEEE TETCI | Link | |
| 2022 | Data Stealing Attack on Medical Images: Is It Safe to Export Networks from Data Lakes? | Medical Image Models | MICCAI | ||
| 2022 | Towards Data-Free Model Stealing in a Hard Label Setting | Machine Learning Models | CVPR | Link | |
| 2022 | Careful What You Wish For: on the Extraction of Adversarially Trained Models | Adversarially Trained Models | PST | Link | |
| 2022 | Enhance Model Stealing Attack via Label Refining | Machine Learning Models | ICSP | Link | |
| 2022 | StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning | Self-Supervised Learning Models | arXiv | Link | |
| 2022 | Demystifying Arch-hints for Model Extraction: An Attack in Unified Memory System | Machine Learning Models | arXiv | Link | |
| 2022 | SNIFF: Reverse Engineering of Neural Networks With Fault Attacks | Neural Networks | IEEE Transactions on Reliability | Link | |
| 2022 | High-Fidelity Model Extraction Attacks via Remote Power Monitors | Machine Learning Models | AICAS | Link | |
| 2022 | User-Level Label Leakage from Gradients in Federated Learning | Federated Learning Models | arXiv | Link | |
| 2022 | Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs | NLP Models | arXiv | Link | |
| 2022 | Effect Verification of a Feature Extraction Method Based on Graph Convolutional Networks | Graph Convolutional Networks | International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM) | Link | |
| 2022 | Towards Extracting Graph Neural Network Models via Prediction Queries (Student Abstract) | Graph Neural Networks | Proceedings of the AAAI Conference on Artificial Intelligence | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2021 | GraphMI: Extracting Private Graph Data from Graph Neural Networks | Graph Neural Networks | IJCAI | Link | |
| 2021 | Extracting Training Data from Large Language Models | Large Language Models | USENIX Security | Link | |
| 2021 | MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation | Machine Learning Models | CVPR | Link | |
| 2021 | Data-Free Model Extraction | Machine Learning Models | CVPR | Link | |
| 2021 | Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! | BERT | arXiv | Link | |
| 2021 | Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization | Graph Neural Networks | arXiv | Link | |
| 2021 | Watermarking Graph Neural Networks by Random Graphs | Graph Neural Networks | ISDFS | Link | |
| 2021 | Model Stealing Attacks Against Inductive Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2021 | Tenet: A Neural Network Model Extraction Attack in Multi-core Architecture | Neural Networks | GLSVLSI | Link | |
| 2021 | Towards Extracting Graph Neural Network Models via Prediction Queries (Student Abstract) | Graph Neural Networks | AAAI | Link | |
| 2021 | GraphMI: Extracting Private Graph Data from Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2021 | Stealing Machine Learning Parameters via Side Channel Power Attacks | Machine Learning Models | ISVLSI | Link | |
| 2021 | Stealing Machine Learning Models: Attacks and Countermeasures for Generative Adversarial Networks | GANs | ACSAC | Link | |
| 2021 | Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack | Machine Learning Models | arXiv | Link | |
| 2021 | SEAT: Similarity Encoder by Adversarial Training for Detecting Model Extraction Attack Queries | Machine Learning Models | AISec | Link | |
| 2021 | Stealing Deep Reinforcement Learning Models for Fun and Profit | Deep Reinforcement Learning Models | ASIA CCS | Link | |
| 2021 | InverseNet: Augmenting Model Extraction Attacks with Training Data Inversion | Machine Learning Models | IJCAI | Link | |
| 2021 | Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction | Sequential Recommenders | RecSys | Link | |
| 2021 | Hermes Attack: Steal DNN Models with Lossless Inference Accuracy | Deep Neural Networks | USENIX Security | Link | |
| 2021 | Deep Neural Network Fingerprinting by Conferrable Adversarial Examples | Deep Neural Networks | arXiv | Link | |
| 2021 | MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI | Explainable AI Models | arXiv | Link | |
| 2021 | Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! | BERT | NAACL | Link | |
| 2021 | Leveraging Partial Model Extractions using Uncertainty Quantification | Machine Learning Models | CloudNet | Link | |
| 2021 | Model Extraction and Adversarial Attacks on Neural Networks Using Switching Power Information | Neural Networks | ICANN | ||
| 2021 | Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs | BERT Models | arXiv | Link | |
| 2021 | Dataset Inference: Ownership Resolution in Machine Learning | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2020 | DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints | Deep Neural Networks | ASPLOS | Link | |
| 2020 | Practical Side-Channel Based Model Extraction Attack on Tree-Based Machine Learning Algorithm | Tree-Based ML Models | ACNS Workshops | ||
| 2020 | Stealing Links from Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2020 | Special-Purpose Model Extraction Attacks: Stealing Coarse Model with Fewer Queries | Machine Learning Models | TrustCom | Link | |
| 2020 | Extraction of Complex DNN Models: Real Threat or Boogeyman? | Deep Neural Networks | EDSMLS | ||
| 2020 | ActiveThief: Model Extraction Using Active Learning and Unannotated Public Data | Machine Learning Models | AAAI | Link | |
| 2020 | Cryptanalytic Extraction of Neural Network Models | Neural Networks | CRYPTO | ||
| 2020 | Stealing Your Data from Compressed Machine Learning Models | Machine Learning Models | DAC | Link | |
| 2020 | Model Extraction Attacks on Recurrent Neural Networks | Recurrent Neural Networks | Journal of Information Processing | ||
| 2020 | Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints | Neural Networks | ASPLOS | Link | |
| 2020 | Leaky DNN: Stealing Deep-Learning Model Secret with GPU Context-Switching Side-Channel | Deep Neural Networks | DSN | Link | |
| 2020 | Best-Effort Adversarial Approximation of Black-Box Malware Classifiers | Malware Classifiers | arXiv | Link | |
| 2020 | High Accuracy and High Fidelity Extraction of Neural Networks | Neural Networks | arXiv | Link | |
| 2020 | Thieves on Sesame Street! Model Extraction of BERT-based APIs | BERT | ICLR | Link | |
| 2020 | Exploring connections between active learning and model extraction | Machine Learning Models | USENIX Security | ||
| 2020 | Black-Box Ripper: Copying black-box models using generative evolutionary algorithms | Machine Learning Models | arXiv | Link | |
| 2020 | Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures | Deep Neural Networks | USENIX Security | Link | |
| 2020 | Open DNN Box by Power Side-Channel Attack | Deep Neural Networks | IEEE TCAS II | Link | |
| 2020 | Reverse-engineering deep ReLU networks | Deep ReLU Networks | ICML | Link | |
| 2020 | Model extraction from counterfactual explanations | Machine Learning Models | arXiv | Link | |
| 2020 | GANRED: GAN-based Reverse Engineering of DNNs via Cache Side-Channel | Deep Neural Networks | CCSW | Link | |
| 2020 | DeepEM: Deep Neural Networks Model Recovery through EM Side-Channel Information Leakage | Deep Neural Networks | HOST | Link | |
| 2020 | A Framework for Evaluating Gradient Leakage Attacks in Federated Learning | Federated Learning Models | arXiv | Link | |
| 2020 | Quantifying (Hyper) Parameter Leakage in Machine Learning | Machine Learning Models | IEEE International Conference on Multimedia Big Data (BigMM) | Link | |
| 2020 | CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples | Deep Learning Models | NDSS | Link | |
| 2020 | Stealing Black-Box Functionality Using The Deep Neural Tree Architecture | Black-Box Models | arXiv | Link | |
| 2020 | Model Extraction Attacks and Defenses on Cloud-Based Machine Learning Models | Cloud-Based ML Models | IEEE Communications Magazine | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2019 | Stealing Neural Networks via Timing Side Channels | Neural Networks | arXiv | Link | |
| 2019 | PRADA: Protecting Against DNN Model Stealing Attacks | Deep Neural Networks | EuroS&P | Link | |
| 2019 | A framework for the extraction of Deep Neural Networks by leveraging public data | Deep Neural Networks | arXiv | Link | |
| 2019 | Adversarial Model Extraction on Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2019 | Efficiently Stealing your Machine Learning Models | Machine Learning Models | WPES | Link | |
| 2019 | GDALR: An Efficient Model Duplication Attack on Black Box Machine Learning Models | Machine Learning Models | ICSCAN | Link | |
| 2019 | Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data | Deep Neural Networks | IJCNN | Link | |
| 2019 | Knockoff Nets: Stealing Functionality of Black-Box Models | Machine Learning Models | CVPR | Link | |
| 2019 | Model Reconstruction from Model Explanations | Machine Learning Models | FAT* | Link | |
| 2019 | Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker | Machine Learning Models | arXiv | Link | |
| 2019 | Model-Extraction Attack Against FPGA-DNN Accelerator Utilizing Correlation Electromagnetic Analysis | DNN Accelerators | FCCM | Link | |
| 2019 | CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel | Neural Networks | USENIX Security | Link | |
| 2019 | Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective | Graph Neural Networks | arXiv | Link | |
| 2019 | Adversarial Examples on Graph Data: Deep Insights into Attack and Defense | Graph Neural Networks | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2018 | Model Extraction Warning in MLaaS Paradigm | Machine Learning Models | ACSAC | Link | |
| 2018 | Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data | CNNs | IJCNN | Link | |
| 2018 | Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls | Machine Learning Models | HST | Link | |
| 2018 | Stealing Hyperparameters in Machine Learning | Machine Learning Models | IEEE S&P | Link | |
| 2018 | Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data | Machine Learning APIs | ISSPIT | Link | |
| 2018 | Towards Reverse-Engineering Black-Box Neural Networks | Neural Networks | arXiv | Link | |
| 2018 | Reverse engineering convolutional neural networks through side-channel information leaks | CNNs | DAC | Link | |
| 2018 | Model Extraction and Active Learning | Machine Learning Models | arXiv | Link | |
| 2018 | Adversarial Attacks on Neural Networks for Graph Data | Graph Neural Networks | SIGKDD | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2017 | Practical Black-Box Attacks against Machine Learning | Machine Learning Models | ASIA CCS | Link | |
| 2017 | Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models | Machine Learning Models | arXiv | Link | |
| 2017 | How to steal a machine learning classifier with deep learning | Machine Learning Classifiers | HST | Link | |
| 2017 | Ensemble Adversarial Training: Attacks and Defenses | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2016 | Stealing Machine Learning Models via Prediction APIs | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2014 | Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering | Support Vector Machines | CIKM | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2024 | Defense Against Model Extraction Attacks on Recommender Systems | Recommender Systems | WSDM | Link | |
| 2024 | GNNGuard: A Fingerprinting Framework for Verifying Ownerships of Graph Neural Networks | Graph Neural Networks | OpenReview | Link | |
| 2024 | Privacy-Enhanced Graph Neural Network for Decentralized Local Graphs | Graph Neural Networks | IEEE TIFS | Link | |
| 2024 | GENIE: Watermarking Graph Neural Networks for Link Prediction | Graph Neural Networks | arXiv | Link | |
| 2024 | PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection | Graph Neural Networks | arXiv | Link | |
| 2024 | Inversion-Guided Defense: Detecting Model Stealing Attacks by Output Inverting | Machine Learning Models | IEEE TIFS | Link | |
| 2024 | Defending against model extraction attacks with OOD feature learning and decision boundary confusion | Machine Learning Models | Computers & Security | Link | |
| 2024 | SAME: Sample Reconstruction against Model Extraction Attacks | Machine Learning Models | AAAI | Link | |
| 2024 | Model Stealing Detection for IoT Services Based on Multi-Dimensional Features | IoT Models | IEEE IoT Journal | Link | |
| 2024 | MisGUIDE : Defense Against Data-Free Deep Learning Model Extraction | Deep Learning Models | arXiv | Link | |
| 2024 | Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples | Machine Learning Models | arXiv | Link | |
| 2024 | Construct a Secure CNN Against Gradient Inversion Attack | CNN | PAKDD | ||
| 2024 | A Comprehensive Defense Framework Against Model Extraction Attacks | Machine Learning Models | IEEE TDSC | Link | |
| 2024 | HODA: Hardness-Oriented Detection of Model Extraction Attacks | Machine Learning Models | IEEE TIFS | Link | |
| 2024 | Adaptive and robust watermark against model extraction attack | Machine Learning Models | arXiv | Link | |
| 2024 | Defense against Model Extraction Attack by Bayesian Active Watermarking | Machine Learning Models | OpenReview | Link | |
| 2024 | Poisoning-Free Defense Against Black-Box Model Extraction | Machine Learning Models | ICASSP | Link | |
| 2024 | Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion | Machine Learning Models | arXiv | Link | |
| 2024 | Efficient Model Stealing Defense with Noise Transition Matrix | Machine Learning Models | CVPR | Link | |
| 2024 | Making models more secure: An efficient model stealing detection method | Machine Learning Models | Computers and Electrical Engineering | Link | |
| 2024 | TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment | Transformer Models | arXiv | Link | |
| 2024 | Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View Data | Deep Neural Networks | arXiv | Link | |
| 2024 | QUEEN: Query Unlearning against Model Extraction | Machine Learning Models | arXiv | Link | |
| 2024 | Bident Structure for Neural Network Model Protection | Neural Networks | SCITEPRESS | Link | |
| 2024 | ModelGuard: Information-Theoretic Defense Against Model Extraction Attacks | Machine Learning Models | USENIX Security | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2023 | GrOVe: Ownership Verification of Graph Neural Networks using Embeddings | Graph Neural Networks | arXiv | Link | |
| 2023 | Making Watermark Survive Model Extraction Attacks in Graph Neural Networks | Graph Neural Networks | |||
| 2023 | Exposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks | Machine Learning Models | CIKM | Link | |
| 2023 | Defending against model extraction attacks with physical unclonable function | Machine Learning Models | Information Sciences | Link | |
| 2023 | Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks | Deep Neural Networks | ACM MM | Link | |
| 2023 | APMSA: Adversarial Perturbation Against Model Stealing Attacks | Machine Learning Models | IEEE TIFS | Link | |
| 2023 | Deep Neural Network Watermarking against Model Extraction Attack | Deep Neural Networks | ACM MM | Link | |
| 2023 | Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders | Encoder Models | NeurIPS | Link | |
| 2023 | Categorical Inference Poisoning: Verifiable Defense Against Black-Box DNN Model Stealing Without Constraining Surrogate Data and Query Times | Deep Neural Networks | IEEE TIFS | Link | |
| 2023 | {GAP}: Differentially Private Graph Neural Networks with Aggregation Perturbation | Graph Neural Networks | USENIX Security Symposium | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2022 | CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks | Text Generation Models | arXiv | Link | |
| 2022 | Defending against Model Stealing via Verifying Embedded External Features | Machine Learning Models | AAAI | Link | |
| 2022 | Monitoring-Based Differential Privacy Mechanism Against Query Flooding-Based Model Extraction Attack | Machine Learning Models | IEEE TDSC | Link | |
| 2022 | DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking | Deep Learning Models | arXiv | Link | |
| 2022 | SeInspect: Defending Model Stealing via Heterogeneous Semantic Inspection | Machine Learning Models | ESORICS | ||
| 2022 | Model Stealing Defense against Exploiting Information Leak through the Interpretation of Deep Neural Nets | Deep Neural Networks | IJCAI | Link | |
| 2022 | How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection | Machine Learning Models | arXiv | Link | |
| 2022 | A Framework for Understanding Model Extraction Attack and Defense | Machine Learning Models | arXiv | Link | |
| 2022 | Increasing the Cost of Model Extraction with Calibrated Proof of Work | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2021 | Watermarking Graph Neural Networks by Random Graphs | Graph Neural Networks | ISDFS | Link | |
| 2021 | BODAME: Bilevel Optimization for Defense Against Model Extraction | Machine Learning Models | arXiv | Link | |
| 2021 | A protection method of trained CNN model with a secret key from unauthorized access | CNN | APSIPA TSIP | Link | |
| 2021 | SEAT: Similarity Encoder by Adversarial Training for Detecting Model Extraction Attack Queries | Machine Learning Models | AISec | Link | |
| 2021 | Entangled Watermarks as a Defense against Model Extraction | Machine Learning Models | USENIX Security | Link | |
| 2021 | Stateful Detection of Model Extraction Attacks | Machine Learning Models | arXiv | Link | |
| 2021 | NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing | Neural Networks | HOST | Link | |
| 2021 | DAS-AST: Defending Against Model Stealing Attacks Based on Adaptive Softmax Transformation | Machine Learning Models | ISC | Link | |
| 2021 | DAWN: Dynamic Adversarial Watermarking of Neural Networks | Neural Networks | ACM MM | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2020 | Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks | Deep Neural Networks | arXiv | Link | |
| 2020 | Perturbing Inputs to Prevent Model Stealing | Machine Learning Models | CNS | Link | |
| 2020 | Defending Against Model Stealing Attacks With Adaptive Misinformation | Machine Learning Models | CVPR | Link | |
| 2020 | Protecting DNNs from Theft using an Ensemble of Diverse Models | Deep Neural Networks | OpenReview | Link | |
| 2020 | A Protection against the Extraction of Neural Network Models | Neural Networks | arXiv | Link | |
| 2020 | Preventing Neural Network Weight Stealing via Network Obfuscation | Neural Networks | Intelligent Computing | ||
| 2020 | Information Laundering for Model Privacy | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2019 | Defending Against Neural Network Model Stealing Attacks Using Deceptive Perturbations | Neural Networks | SPW | Link | |
| 2019 | PRADA: Protecting Against DNN Model Stealing Attacks | Deep Neural Networks | EuroS&P | Link | |
| 2019 | BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks | Machine Learning Models | ESORICS | ||
| 2019 | MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network | Social Network Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2018 | Model Extraction Warning in MLaaS Paradigm | Machine Learning Models | ACSAC | Link | |
| 2018 | Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks | Deep Neural Networks | Research in Attacks, Intrusions, and Defenses |
Model Extraction(Stealing) Attacks and Defenses on Machine Learning Models Literature
33
20 commits
updated Sep 25, 2024
This repository contains a curated list of research papers on model extraction attacks and defenses in machine learning, organized by year of publication.
Papers are sorted by their released dates in descending order.
| Year | Title | Venue | Paper Link | Code Link |
|---|---|---|---|---|
| 2024 | A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges | arXiv | Link | |
| 2024 | Graph neural networks: a survey on the links between privacy and security | Artificial Intelligence Review | Link | |
| 2024 | Trustworthy Graph Neural Networks: Aspects, Methods and Trends | Proceedings of the IEEE | Link | |
| 2024 | Safety in Graph Machine Learning: Threats and Safeguards | arXiv | Link | |
| 2024 | SoK: All You Need to Know About On-Device ML Model Extraction - The Gap Between Research and Practice | USENIX | Link | |
| 2024 | Unique Security and Privacy Threats of Large Language Model: A Comprehensive Survey | arXiv | Link | |
| 2023 | A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications | arXiv | Link | |
| 2023 | A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability | arXiv | Link | |
| 2023 | A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly | ScienceDirect | Link | |
| 2023 | I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences | ACM Computing Surveys | Link | |
| 2023 | A Taxonomic Survey of Model Extraction Attacks | IEEE International Conference on Cyber Security and Resilience (CSR) | Link | |
| 2023 | A Survey of Privacy Attacks in Machine Learning | ACM Computing Surveys | Link | |
| 2023 | Adversarial Attack and Defense on Graph Data: A Survey | IEEE Transactions on Knowledge and Data Engineering | Link | |
| 2023 | Model Extraction Attacks Revisited | arXiv | https://arxiv.org/abs/2312.05386 | |
| 2022 | Privacy and Robustness in Federated Learning: Attacks and Defenses | IEEE Transactions on Neural Networks and Learning Systems | Link | |
| 2022 | Towards Security Threats of Deep Learning Systems: A Survey | IEEE Transactions on Software Engineering | Link | |
| 2021 | A Critical Overview of Privacy in Machine Learning | IEEE Security & Privacy | Link | |
| 2021 | When Machine Learning Meets Privacy: A Survey and Outlook | ACM Computing Surveys | Link | |
| 2020 | An Overview of Privacy in Machine Learning | arXiv | Link | |
| 2020 | Privacy-preserving deep learning on machine learning as a service—a comprehensive survey | IEEE Access | Link | |
| 2020 | Privacy and security issues in deep learning: A survey | IEEE Access | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2024 | Special Characters Attack: Toward Scalable Training Data Extraction From Large Language Models | Large Language Models | arXiv | Link | |
| 2024 | LLM-FIN: Large Language Models Fingerprinting Attack on Edge Devices | Large Language Models | ISQED | Link | |
| 2024 | Model Extraction Attack against On-device Deep Learning with Power Side Channel | Deep Neural Networks | ISQED | Link | |
| 2024 | Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2024 | A realistic model extraction attack against graph neural networks | Graph Neural Networks | Knowledge-Based Systems | Link | |
| 2024 | Large Language Models for Link Stealing Attacks Against Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2024 | Link Stealing Attacks Against Inductive Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2024 | Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels | CNN | arXiv | Link | |
| 2024 | SwiftTheft: A Time-Efficient Model Extraction Attack Framework Against Cloud-Based Deep Neural Networks | Deep Neural Networks | Chinese Journal of Electronics | Link | |
| 2024 | Model Extraction Attack Without Natural Images | Deep Neural Networks | ACNS Workshops | ||
| 2024 | DEMISTIFY: Identifying On-device Machine Learning Models Stealing and Reuse Vulnerabilities in Mobile Apps | Mobile ML Models | ICSE | Link | |
| 2024 | Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble Based Sample Selection | Deep Neural Networks | WACV | Link | |
| 2024 | Trained to Leak: Hiding Trojan Side-Channels in Neural Network Weights | Neural Networks | HOST | Link | |
| 2024 | A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning | Split Learning Models | CVPR | Link | |
| 2024 | Prompt Stealing Attacks Against Large Language Models | Large Language Models | arXiv | Link | |
| 2024 | Stealing Part of a Production Language Model | Large Language Models | arXiv | Link | |
| 2024 | Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing | Deep Neural Networks | CVPR | Link | |
| 2024 | Teach LLMs to Phish: Stealing Private Information from Language Models | Large Language Models | arXiv | Link | |
| 2024 | Data-Free Hard-Label Robustness Stealing Attack | Deep Neural Networks | AAAI | Link | |
| 2024 | Large Language Model Watermark Stealing With Mixed Integer Programming | Large Language Models | arXiv | Link | |
| 2024 | PRSA: PRompt Stealing Attacks against Large Language Models | Large Language Models | arXiv | Link | |
| 2024 | QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines | Quantum Neural Networks | arXiv | Link | |
| 2024 | Privacy Backdoors: Stealing Data with Corrupted Pretrained Models | Pretrained Models | arXiv | Link | |
| 2024 | AugSteal: Advancing Model Steal With Data Augmentation in Active Learning Frameworks | Active Learning Models | IEEE TIFS | Link | |
| 2024 | Towards Model Extraction Attacks in GAN-Based Image Translation via Domain Shift Mitigation | GANs | AAAI | Link | |
| 2024 | Stealing Image-to-Image Translation Models With a Single Query | Image-to-Image Translation Models | arXiv | Link | |
| 2024 | A two-stage model extraction attack on GANs with a small collected dataset | GANs | Computers & Security | Link | |
| 2024 | Layer Sequence Extraction of Optimized DNNs Using Side-Channel Information Leaks | Deep Neural Networks | IEEE TCAD | Link | |
| 2024 | COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability | Large Language Models | arXiv | Link | |
| 2024 | Prompt Stealing Attacks Against Text-to-Image Generation Models | Text-to-Image Models | arXiv | Link | |
| 2024 | An Empirical Evaluation of the Data Leakage in Federated Graph Learning | Graph Neural Networks | IEEE Transactions on Network Science and Engineering | Paper | |
| 2024 | AttacKG+:Boosting Attack Knowledge Graph Construction with Large Language Models | Knowledge Graphs | arXiv | Link | |
| 2024 | Adversarial Attacks on Fairness of Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2024 | Unveiling Memorization in Code Models | Code Models | ICSE | Link | |
| 2024 | Unveiling the Secrets without Data: Can Graph Neural Networks Be Exploited through Data-Free Model Extraction Attacks? | Graph Neural Networks | USENIX Security | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2023 | Explanation leaks: Explanation-guided model extraction attacks | Explainable AI Models | Information Sciences | Link | |
| 2023 | Model Leeching: An Extraction Attack Targeting LLMs | Large Language Models | arXiv | Link | |
| 2023 | Are Diffusion Models Vulnerable to Membership Inference Attacks? | Diffusion Models | ICML | Link | |
| 2023 | Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation | Large Language Models | arXiv | Link | |
| 2023 | Model Extraction Attacks on Split Federated Learning | Federated Learning Models | arXiv | Link | |
| 2023 | Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders | Image Encoders | arXiv | Link | |
| 2023 | Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity | Graph Neural Networks | arXiv | Link | |
| 2023 | Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack | Machine Learning Models | NeurIPS | Link | |
| 2023 | D-DAE: Defense-Penetrating Model Extraction Attacks | Deep Neural Networks | IEEE S&P | Link | |
| 2023 | Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models | Image Translation Models | arXiv | Link | |
| 2023 | AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against White-Box Models | White-Box Models | arXiv | Link | |
| 2023 | Efficient Model Extraction by Data Set Stealing, Balancing, and Filtering | Machine Learning Models | IEEE IoT Journal | Link | |
| 2023 | DivTheft: An Ensemble Model Stealing Attack by Divide-and-Conquer | Machine Learning Models | IEEE TDSC | Link | |
| 2023 | Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks | Large Language Models | arXiv | Link | |
| 2023 | A Theoretical Insight into Attack and Defense of Gradient Leakage in Transformer | Transformer Models | arXiv | Link | |
| 2023 | When the Curious Abandon Honesty: Federated Learning Is Not Private | Federated Learning Models | IEEE European Symposium on Security and Privacy (EuroS&P) | Link | |
| 2023 | Making Watermark Survive Model Extraction Attacks in Graph Neural Networks | Graph Neural Networks | IEEE International Conference on Communications | Link | |
| 2023 | A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots | Machine Learning Models | arXiv | Link | |
| 2023 | Private Graph Extraction via Feature Explanations | Graph Neural Networks | Proceedings on Privacy Enhancing Technologies | Link | |
| 2023 | Extracting Privacy-Preserving Subgraphs in Federated Graph Learning using Information Bottleneck | Federated Graph Models | ACM Asia CCS | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2022 | Canary Extraction in Natural Language Understanding Models | NLU Models | arXiv | Link | |
| 2022 | Are Large Pre-Trained Language Models Leaking Your Personal Information? | Large Language Models | arXiv | Link | |
| 2022 | Text Revealer: Private Text Reconstruction via Model Inversion Attacks against Transformers | Transformers | arXiv | Link | |
| 2022 | Precise Extraction of Deep Learning Models via Side-Channel Attacks on Edge/Endpoint Devices | Deep Learning Models | ESORICS | ||
| 2022 | DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories | Deep Neural Networks | IEEE S&P | Link | |
| 2022 | DualCF: Efficient Model Extraction Attack from Counterfactual Explanations | Machine Learning Models | FAccT | Link | |
| 2022 | GAME: Generative-Based Adaptive Model Extraction Attack | Machine Learning Models | ESORICS | ||
| 2022 | Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks | Deep Neural Networks | NeurIPS | Link | |
| 2022 | On the Difficulty of Defending Self-Supervised Learning against Model Extraction | Self-Supervised Learning Models | ICML | Link | |
| 2022 | Black-Box Dissector: Towards Erasing-Based Hard-Label Model Stealing Attack | Machine Learning Models | ECCV | ||
| 2022 | Imitated Detectors: Stealing Knowledge of Black-box Object Detectors | Object Detectors | ACM MM | Link | |
| 2022 | StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning | Self-Supervised Learning Models | ACM CCS | Link | |
| 2022 | Model Stealing Attacks Against Vision-Language Models | Vision-Language Models | OpenReview | Link | |
| 2022 | ES Attack: Model Stealing Against Deep Neural Networks Without Data Hurdles | Deep Neural Networks | IEEE TETCI | Link | |
| 2022 | Data Stealing Attack on Medical Images: Is It Safe to Export Networks from Data Lakes? | Medical Image Models | MICCAI | ||
| 2022 | Towards Data-Free Model Stealing in a Hard Label Setting | Machine Learning Models | CVPR | Link | |
| 2022 | Careful What You Wish For: on the Extraction of Adversarially Trained Models | Adversarially Trained Models | PST | Link | |
| 2022 | Enhance Model Stealing Attack via Label Refining | Machine Learning Models | ICSP | Link | |
| 2022 | StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning | Self-Supervised Learning Models | arXiv | Link | |
| 2022 | Demystifying Arch-hints for Model Extraction: An Attack in Unified Memory System | Machine Learning Models | arXiv | Link | |
| 2022 | SNIFF: Reverse Engineering of Neural Networks With Fault Attacks | Neural Networks | IEEE Transactions on Reliability | Link | |
| 2022 | High-Fidelity Model Extraction Attacks via Remote Power Monitors | Machine Learning Models | AICAS | Link | |
| 2022 | User-Level Label Leakage from Gradients in Federated Learning | Federated Learning Models | arXiv | Link | |
| 2022 | Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs | NLP Models | arXiv | Link | |
| 2022 | Effect Verification of a Feature Extraction Method Based on Graph Convolutional Networks | Graph Convolutional Networks | International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM) | Link | |
| 2022 | Towards Extracting Graph Neural Network Models via Prediction Queries (Student Abstract) | Graph Neural Networks | Proceedings of the AAAI Conference on Artificial Intelligence | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2021 | GraphMI: Extracting Private Graph Data from Graph Neural Networks | Graph Neural Networks | IJCAI | Link | |
| 2021 | Extracting Training Data from Large Language Models | Large Language Models | USENIX Security | Link | |
| 2021 | MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation | Machine Learning Models | CVPR | Link | |
| 2021 | Data-Free Model Extraction | Machine Learning Models | CVPR | Link | |
| 2021 | Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! | BERT | arXiv | Link | |
| 2021 | Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization | Graph Neural Networks | arXiv | Link | |
| 2021 | Watermarking Graph Neural Networks by Random Graphs | Graph Neural Networks | ISDFS | Link | |
| 2021 | Model Stealing Attacks Against Inductive Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2021 | Tenet: A Neural Network Model Extraction Attack in Multi-core Architecture | Neural Networks | GLSVLSI | Link | |
| 2021 | Towards Extracting Graph Neural Network Models via Prediction Queries (Student Abstract) | Graph Neural Networks | AAAI | Link | |
| 2021 | GraphMI: Extracting Private Graph Data from Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2021 | Stealing Machine Learning Parameters via Side Channel Power Attacks | Machine Learning Models | ISVLSI | Link | |
| 2021 | Stealing Machine Learning Models: Attacks and Countermeasures for Generative Adversarial Networks | GANs | ACSAC | Link | |
| 2021 | Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack | Machine Learning Models | arXiv | Link | |
| 2021 | SEAT: Similarity Encoder by Adversarial Training for Detecting Model Extraction Attack Queries | Machine Learning Models | AISec | Link | |
| 2021 | Stealing Deep Reinforcement Learning Models for Fun and Profit | Deep Reinforcement Learning Models | ASIA CCS | Link | |
| 2021 | InverseNet: Augmenting Model Extraction Attacks with Training Data Inversion | Machine Learning Models | IJCAI | Link | |
| 2021 | Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction | Sequential Recommenders | RecSys | Link | |
| 2021 | Hermes Attack: Steal DNN Models with Lossless Inference Accuracy | Deep Neural Networks | USENIX Security | Link | |
| 2021 | Deep Neural Network Fingerprinting by Conferrable Adversarial Examples | Deep Neural Networks | arXiv | Link | |
| 2021 | MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI | Explainable AI Models | arXiv | Link | |
| 2021 | Model Extraction and Adversarial Transferability, Your BERT is Vulnerable! | BERT | NAACL | Link | |
| 2021 | Leveraging Partial Model Extractions using Uncertainty Quantification | Machine Learning Models | CloudNet | Link | |
| 2021 | Model Extraction and Adversarial Attacks on Neural Networks Using Switching Power Information | Neural Networks | ICANN | ||
| 2021 | Killing One Bird with Two Stones: Model Extraction and Attribute Inference Attacks against BERT-based APIs | BERT Models | arXiv | Link | |
| 2021 | Dataset Inference: Ownership Resolution in Machine Learning | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2020 | DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints | Deep Neural Networks | ASPLOS | Link | |
| 2020 | Practical Side-Channel Based Model Extraction Attack on Tree-Based Machine Learning Algorithm | Tree-Based ML Models | ACNS Workshops | ||
| 2020 | Stealing Links from Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2020 | Special-Purpose Model Extraction Attacks: Stealing Coarse Model with Fewer Queries | Machine Learning Models | TrustCom | Link | |
| 2020 | Extraction of Complex DNN Models: Real Threat or Boogeyman? | Deep Neural Networks | EDSMLS | ||
| 2020 | ActiveThief: Model Extraction Using Active Learning and Unannotated Public Data | Machine Learning Models | AAAI | Link | |
| 2020 | Cryptanalytic Extraction of Neural Network Models | Neural Networks | CRYPTO | ||
| 2020 | Stealing Your Data from Compressed Machine Learning Models | Machine Learning Models | DAC | Link | |
| 2020 | Model Extraction Attacks on Recurrent Neural Networks | Recurrent Neural Networks | Journal of Information Processing | ||
| 2020 | Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints | Neural Networks | ASPLOS | Link | |
| 2020 | Leaky DNN: Stealing Deep-Learning Model Secret with GPU Context-Switching Side-Channel | Deep Neural Networks | DSN | Link | |
| 2020 | Best-Effort Adversarial Approximation of Black-Box Malware Classifiers | Malware Classifiers | arXiv | Link | |
| 2020 | High Accuracy and High Fidelity Extraction of Neural Networks | Neural Networks | arXiv | Link | |
| 2020 | Thieves on Sesame Street! Model Extraction of BERT-based APIs | BERT | ICLR | Link | |
| 2020 | Exploring connections between active learning and model extraction | Machine Learning Models | USENIX Security | ||
| 2020 | Black-Box Ripper: Copying black-box models using generative evolutionary algorithms | Machine Learning Models | arXiv | Link | |
| 2020 | Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures | Deep Neural Networks | USENIX Security | Link | |
| 2020 | Open DNN Box by Power Side-Channel Attack | Deep Neural Networks | IEEE TCAS II | Link | |
| 2020 | Reverse-engineering deep ReLU networks | Deep ReLU Networks | ICML | Link | |
| 2020 | Model extraction from counterfactual explanations | Machine Learning Models | arXiv | Link | |
| 2020 | GANRED: GAN-based Reverse Engineering of DNNs via Cache Side-Channel | Deep Neural Networks | CCSW | Link | |
| 2020 | DeepEM: Deep Neural Networks Model Recovery through EM Side-Channel Information Leakage | Deep Neural Networks | HOST | Link | |
| 2020 | A Framework for Evaluating Gradient Leakage Attacks in Federated Learning | Federated Learning Models | arXiv | Link | |
| 2020 | Quantifying (Hyper) Parameter Leakage in Machine Learning | Machine Learning Models | IEEE International Conference on Multimedia Big Data (BigMM) | Link | |
| 2020 | CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples | Deep Learning Models | NDSS | Link | |
| 2020 | Stealing Black-Box Functionality Using The Deep Neural Tree Architecture | Black-Box Models | arXiv | Link | |
| 2020 | Model Extraction Attacks and Defenses on Cloud-Based Machine Learning Models | Cloud-Based ML Models | IEEE Communications Magazine | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2019 | Stealing Neural Networks via Timing Side Channels | Neural Networks | arXiv | Link | |
| 2019 | PRADA: Protecting Against DNN Model Stealing Attacks | Deep Neural Networks | EuroS&P | Link | |
| 2019 | A framework for the extraction of Deep Neural Networks by leveraging public data | Deep Neural Networks | arXiv | Link | |
| 2019 | Adversarial Model Extraction on Graph Neural Networks | Graph Neural Networks | arXiv | Link | |
| 2019 | Efficiently Stealing your Machine Learning Models | Machine Learning Models | WPES | Link | |
| 2019 | GDALR: An Efficient Model Duplication Attack on Black Box Machine Learning Models | Machine Learning Models | ICSCAN | Link | |
| 2019 | Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data | Deep Neural Networks | IJCNN | Link | |
| 2019 | Knockoff Nets: Stealing Functionality of Black-Box Models | Machine Learning Models | CVPR | Link | |
| 2019 | Model Reconstruction from Model Explanations | Machine Learning Models | FAT* | Link | |
| 2019 | Model Weight Theft With Just Noise Inputs: The Curious Case of the Petulant Attacker | Machine Learning Models | arXiv | Link | |
| 2019 | Model-Extraction Attack Against FPGA-DNN Accelerator Utilizing Correlation Electromagnetic Analysis | DNN Accelerators | FCCM | Link | |
| 2019 | CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel | Neural Networks | USENIX Security | Link | |
| 2019 | Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective | Graph Neural Networks | arXiv | Link | |
| 2019 | Adversarial Examples on Graph Data: Deep Insights into Attack and Defense | Graph Neural Networks | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2018 | Model Extraction Warning in MLaaS Paradigm | Machine Learning Models | ACSAC | Link | |
| 2018 | Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data | CNNs | IJCNN | Link | |
| 2018 | Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls | Machine Learning Models | HST | Link | |
| 2018 | Stealing Hyperparameters in Machine Learning | Machine Learning Models | IEEE S&P | Link | |
| 2018 | Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data | Machine Learning APIs | ISSPIT | Link | |
| 2018 | Towards Reverse-Engineering Black-Box Neural Networks | Neural Networks | arXiv | Link | |
| 2018 | Reverse engineering convolutional neural networks through side-channel information leaks | CNNs | DAC | Link | |
| 2018 | Model Extraction and Active Learning | Machine Learning Models | arXiv | Link | |
| 2018 | Adversarial Attacks on Neural Networks for Graph Data | Graph Neural Networks | SIGKDD | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2017 | Practical Black-Box Attacks against Machine Learning | Machine Learning Models | ASIA CCS | Link | |
| 2017 | Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models | Machine Learning Models | arXiv | Link | |
| 2017 | How to steal a machine learning classifier with deep learning | Machine Learning Classifiers | HST | Link | |
| 2017 | Ensemble Adversarial Training: Attacks and Defenses | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2016 | Stealing Machine Learning Models via Prediction APIs | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2014 | Adding Robustness to Support Vector Machines Against Adversarial Reverse Engineering | Support Vector Machines | CIKM | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2024 | Defense Against Model Extraction Attacks on Recommender Systems | Recommender Systems | WSDM | Link | |
| 2024 | GNNGuard: A Fingerprinting Framework for Verifying Ownerships of Graph Neural Networks | Graph Neural Networks | OpenReview | Link | |
| 2024 | Privacy-Enhanced Graph Neural Network for Decentralized Local Graphs | Graph Neural Networks | IEEE TIFS | Link | |
| 2024 | GENIE: Watermarking Graph Neural Networks for Link Prediction | Graph Neural Networks | arXiv | Link | |
| 2024 | PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection | Graph Neural Networks | arXiv | Link | |
| 2024 | Inversion-Guided Defense: Detecting Model Stealing Attacks by Output Inverting | Machine Learning Models | IEEE TIFS | Link | |
| 2024 | Defending against model extraction attacks with OOD feature learning and decision boundary confusion | Machine Learning Models | Computers & Security | Link | |
| 2024 | SAME: Sample Reconstruction against Model Extraction Attacks | Machine Learning Models | AAAI | Link | |
| 2024 | Model Stealing Detection for IoT Services Based on Multi-Dimensional Features | IoT Models | IEEE IoT Journal | Link | |
| 2024 | MisGUIDE : Defense Against Data-Free Deep Learning Model Extraction | Deep Learning Models | arXiv | Link | |
| 2024 | Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples | Machine Learning Models | arXiv | Link | |
| 2024 | Construct a Secure CNN Against Gradient Inversion Attack | CNN | PAKDD | ||
| 2024 | A Comprehensive Defense Framework Against Model Extraction Attacks | Machine Learning Models | IEEE TDSC | Link | |
| 2024 | HODA: Hardness-Oriented Detection of Model Extraction Attacks | Machine Learning Models | IEEE TIFS | Link | |
| 2024 | Adaptive and robust watermark against model extraction attack | Machine Learning Models | arXiv | Link | |
| 2024 | Defense against Model Extraction Attack by Bayesian Active Watermarking | Machine Learning Models | OpenReview | Link | |
| 2024 | Poisoning-Free Defense Against Black-Box Model Extraction | Machine Learning Models | ICASSP | Link | |
| 2024 | Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion | Machine Learning Models | arXiv | Link | |
| 2024 | Efficient Model Stealing Defense with Noise Transition Matrix | Machine Learning Models | CVPR | Link | |
| 2024 | Making models more secure: An efficient model stealing detection method | Machine Learning Models | Computers and Electrical Engineering | Link | |
| 2024 | TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment | Transformer Models | arXiv | Link | |
| 2024 | Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View Data | Deep Neural Networks | arXiv | Link | |
| 2024 | QUEEN: Query Unlearning against Model Extraction | Machine Learning Models | arXiv | Link | |
| 2024 | Bident Structure for Neural Network Model Protection | Neural Networks | SCITEPRESS | Link | |
| 2024 | ModelGuard: Information-Theoretic Defense Against Model Extraction Attacks | Machine Learning Models | USENIX Security | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2023 | GrOVe: Ownership Verification of Graph Neural Networks using Embeddings | Graph Neural Networks | arXiv | Link | |
| 2023 | Making Watermark Survive Model Extraction Attacks in Graph Neural Networks | Graph Neural Networks | |||
| 2023 | Exposing Model Theft: A Robust and Transferable Watermark for Thwarting Model Extraction Attacks | Machine Learning Models | CIKM | Link | |
| 2023 | Defending against model extraction attacks with physical unclonable function | Machine Learning Models | Information Sciences | Link | |
| 2023 | Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks | Deep Neural Networks | ACM MM | Link | |
| 2023 | APMSA: Adversarial Perturbation Against Model Stealing Attacks | Machine Learning Models | IEEE TIFS | Link | |
| 2023 | Deep Neural Network Watermarking against Model Extraction Attack | Deep Neural Networks | ACM MM | Link | |
| 2023 | Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders | Encoder Models | NeurIPS | Link | |
| 2023 | Categorical Inference Poisoning: Verifiable Defense Against Black-Box DNN Model Stealing Without Constraining Surrogate Data and Query Times | Deep Neural Networks | IEEE TIFS | Link | |
| 2023 | {GAP}: Differentially Private Graph Neural Networks with Aggregation Perturbation | Graph Neural Networks | USENIX Security Symposium | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2022 | CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks | Text Generation Models | arXiv | Link | |
| 2022 | Defending against Model Stealing via Verifying Embedded External Features | Machine Learning Models | AAAI | Link | |
| 2022 | Monitoring-Based Differential Privacy Mechanism Against Query Flooding-Based Model Extraction Attack | Machine Learning Models | IEEE TDSC | Link | |
| 2022 | DynaMarks: Defending Against Deep Learning Model Extraction Using Dynamic Watermarking | Deep Learning Models | arXiv | Link | |
| 2022 | SeInspect: Defending Model Stealing via Heterogeneous Semantic Inspection | Machine Learning Models | ESORICS | ||
| 2022 | Model Stealing Defense against Exploiting Information Leak through the Interpretation of Deep Neural Nets | Deep Neural Networks | IJCAI | Link | |
| 2022 | How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection | Machine Learning Models | arXiv | Link | |
| 2022 | A Framework for Understanding Model Extraction Attack and Defense | Machine Learning Models | arXiv | Link | |
| 2022 | Increasing the Cost of Model Extraction with Calibrated Proof of Work | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2021 | Watermarking Graph Neural Networks by Random Graphs | Graph Neural Networks | ISDFS | Link | |
| 2021 | BODAME: Bilevel Optimization for Defense Against Model Extraction | Machine Learning Models | arXiv | Link | |
| 2021 | A protection method of trained CNN model with a secret key from unauthorized access | CNN | APSIPA TSIP | Link | |
| 2021 | SEAT: Similarity Encoder by Adversarial Training for Detecting Model Extraction Attack Queries | Machine Learning Models | AISec | Link | |
| 2021 | Entangled Watermarks as a Defense against Model Extraction | Machine Learning Models | USENIX Security | Link | |
| 2021 | Stateful Detection of Model Extraction Attacks | Machine Learning Models | arXiv | Link | |
| 2021 | NeurObfuscator: A Full-stack Obfuscation Tool to Mitigate Neural Architecture Stealing | Neural Networks | HOST | Link | |
| 2021 | DAS-AST: Defending Against Model Stealing Attacks Based on Adaptive Softmax Transformation | Machine Learning Models | ISC | Link | |
| 2021 | DAWN: Dynamic Adversarial Watermarking of Neural Networks | Neural Networks | ACM MM | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2020 | Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks | Deep Neural Networks | arXiv | Link | |
| 2020 | Perturbing Inputs to Prevent Model Stealing | Machine Learning Models | CNS | Link | |
| 2020 | Defending Against Model Stealing Attacks With Adaptive Misinformation | Machine Learning Models | CVPR | Link | |
| 2020 | Protecting DNNs from Theft using an Ensemble of Diverse Models | Deep Neural Networks | OpenReview | Link | |
| 2020 | A Protection against the Extraction of Neural Network Models | Neural Networks | arXiv | Link | |
| 2020 | Preventing Neural Network Weight Stealing via Network Obfuscation | Neural Networks | Intelligent Computing | ||
| 2020 | Information Laundering for Model Privacy | Machine Learning Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2019 | Defending Against Neural Network Model Stealing Attacks Using Deceptive Perturbations | Neural Networks | SPW | Link | |
| 2019 | PRADA: Protecting Against DNN Model Stealing Attacks | Deep Neural Networks | EuroS&P | Link | |
| 2019 | BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks | Machine Learning Models | ESORICS | ||
| 2019 | MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network | Social Network Models | arXiv | Link |
| Year | Title | Target Model | Venue | Paper Link | Code Link |
|---|---|---|---|---|---|
| 2018 | Model Extraction Warning in MLaaS Paradigm | Machine Learning Models | ACSAC | Link | |
| 2018 | Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks | Deep Neural Networks | Research in Attacks, Intrusions, and Defenses |
