Image Forgery Detection and Localization (and related) Papers List
See the codeccf-rankings now marked with different colors(
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Newly added papers will be organized at the top of every category now.
骨干网络,多为图像分类的网络。
图像篡改检测定位
Some of the above papers also contain methods to detect tampered images generated by GANs or DMs or LLMs related for synthetic images
Frequency-aware Correlation Discovering and Spatial Forgery Clue Distilling for Synthetic Image Detection
ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection
Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
MFF-Net: A multi-view feature fusion network for generalized forgery image detection
STD-FD: Spatio-Temporal Distribution Fitting Deviation for AIGC Forgery Identification
So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection
Adversarially Robust AI-Generated Image Detection for Free: An Information Theoretic Perspective
Spatial-Temporal Reconstruction Error for AIGC-based Forgery Image Detection
Noise-Informed Diffusion-Generated Image Detection With Anomaly Attention
FAMSeC: A Few-Shot-Sample-Based General AI-Generated Image Detection Method
FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics
Exploring the Collaborative Advantage of Low-level Information on Generalizable AI-Generated Image Detection
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era
Can GPT tell us why these images are synthesized? Empowering Multimodal Large Language Models for Forensics
Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach
FakeReasoning: Towards Generalizable Forgery Detection and Reasoning
Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation
LEGION: Learning to Ground and Explain for Synthetic Image Detection
Survey on AI-Generated Media Detection: From Non-MLLM to MLLM
MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection (CVPR '24) [Paper]
Preserving Fairness Generalization in Deepfake Detection (CVPR '24) [Paper] [Code]
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection (ECCV '24) [Paper] [Code]
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection (arXiv '23) [Paper]
AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors (arXiv '23) [Paper] [Code]
MaLP: Manipulation Localization Using a Proactive Scheme (CVPR '23) [Paper] [Code]
Discrepancy-Guided Reconstruction Learning for Image Forgery Detection (IJCAI '23) [Paper] [Code]
Generalizable Synthetic Image Detection via Language-guided Contrastive Learning (arXiv '23) [Paper] [Code]
Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization (arXiv '23) [Paper] [Code]
Discrepancy-Guided Reconstruction Learning for Image Forgery Detection (arXiv '23) [Paper]
Masked Relation Learning for DeepFake Detection (TIFS '23) [Paper]
图像的拼接篡改检测定位
图像协调化
人脸篡改,篡改方法以及检测问题
复制移动篡改定位问题
图像中的文本篡改检测问题 (parts of)
Related resources:
Low-level tasks include super-resolution, denoise, dehze, low-light enhancement, etc. High-level tasks include classification, detection, segmentation, etc. segmentation, and so on. However, the ones I have listed here are probably still mainly related to tampering detection.
Testing the new layout of paper title.
📖Paper, 👨💻Code, 📦Dataset, 🔗Other links, 📜News,
*Equal contribution. #Corresponding author.
Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
(EVP) Explicit Visual Prompting for Low-Level Structure Segmentations (CVPR '23) 📖, 👨💻 (including defocus blur, shadow, forgery, camouflaged dection)
Weihuang Liu1, Xi Shen2, Chi-Man Pun#,1, Xiaodong Cun#,2
1University of Macau 2Tencent AI Lab
SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device (ICCV '23) 📖, 👨💻
Weiran Gou∗1,2, Ziyao Yi∗1,2, Yan Xiang1,2, Shaoqing Li1,2, Zibin Liu1,2, Dehui Kong1,2, Ke Xu#1,2
1State Key Laboratory of Mobile Network and Mobile Multimedia Technology, 2Sanechips Technology, Chengdu, China
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision (ICLR '24_) 📖, 👨💻
Haoning Wu1*, Zicheng Zhang2*, Erli Zhang1*, Chaofeng Chen1, Liang Liao1, Annan Wang1, Chunyi Li2, Wenxiu Sun3, Qiong Yan3, Guangtao Zhai2, Weisi Lin1#
1Nanyang Technological University, 2Shanghai Jiaotong University, 3Sensetime Research
特征匹配,图像匹配问题。
目标检测,包括伪装物体目标检测和突出目标检测,COD以及SOD。
语义分割,将图片中完整语义(具有标签或者类别)的部分分割出来。不仅要进行目标检测检测到图像中的物体,还需要对每个像素分类。
异常检测,通常用于发现与正常模式或预期模式不符的图像与视频。
For the convenience of readers in checking the information of each paper, I have used different colors to mark the ranking of the conferences or journals where each paper was published on the CCF(China Computer Federation) Recommended List of International Conferences and Periodicals: A is marked in red, B in yellow, C in green, and sources not included are marked in grey. All articles are the result of the researchers' hard work, and the development and progress in the field of image tampering detection and localization cannot be separated from these outstanding researchers. "It should be pointed out that the recommended List by CCF for professionals and researchers on computing to publish their findings and results, is not sole criteria for academic evaluation, but as a suggestion or reference for the industry." ↩
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Image Forgery Detection and Localization (and related) Papers List
See the codeccf-rankings now marked with different colors(
![]()
![]()
![]()
)1
Newly added papers will be organized at the top of every category now.
骨干网络,多为图像分类的网络。
图像篡改检测定位
Some of the above papers also contain methods to detect tampered images generated by GANs or DMs or LLMs related for synthetic images
Frequency-aware Correlation Discovering and Spatial Forgery Clue Distilling for Synthetic Image Detection
ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detection
Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
MFF-Net: A multi-view feature fusion network for generalized forgery image detection
STD-FD: Spatio-Temporal Distribution Fitting Deviation for AIGC Forgery Identification
So-Fake: Benchmarking and Explaining Social Media Image Forgery Detection
Adversarially Robust AI-Generated Image Detection for Free: An Information Theoretic Perspective
Spatial-Temporal Reconstruction Error for AIGC-based Forgery Image Detection
Noise-Informed Diffusion-Generated Image Detection With Anomaly Attention
FAMSeC: A Few-Shot-Sample-Based General AI-Generated Image Detection Method
FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics
Exploring the Collaborative Advantage of Low-level Information on Generalizable AI-Generated Image Detection
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era
Can GPT tell us why these images are synthesized? Empowering Multimodal Large Language Models for Forensics
Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach
FakeReasoning: Towards Generalizable Forgery Detection and Reasoning
Spot the Fake: Large Multimodal Model-Based Synthetic Image Detection with Artifact Explanation
LEGION: Learning to Ground and Explain for Synthetic Image Detection
Survey on AI-Generated Media Detection: From Non-MLLM to MLLM
MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection (CVPR '24) [Paper]
Preserving Fairness Generalization in Deepfake Detection (CVPR '24) [Paper] [Code]
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection (ECCV '24) [Paper] [Code]
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection (arXiv '23) [Paper]
AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors (arXiv '23) [Paper] [Code]
MaLP: Manipulation Localization Using a Proactive Scheme (CVPR '23) [Paper] [Code]
Discrepancy-Guided Reconstruction Learning for Image Forgery Detection (IJCAI '23) [Paper] [Code]
Generalizable Synthetic Image Detection via Language-guided Contrastive Learning (arXiv '23) [Paper] [Code]
Detect Any Deepfakes: Segment Anything Meets Face Forgery Detection and Localization (arXiv '23) [Paper] [Code]
Discrepancy-Guided Reconstruction Learning for Image Forgery Detection (arXiv '23) [Paper]
Masked Relation Learning for DeepFake Detection (TIFS '23) [Paper]
图像的拼接篡改检测定位
图像协调化
人脸篡改,篡改方法以及检测问题
复制移动篡改定位问题
图像中的文本篡改检测问题 (parts of)
Related resources:
Low-level tasks include super-resolution, denoise, dehze, low-light enhancement, etc. High-level tasks include classification, detection, segmentation, etc. segmentation, and so on. However, the ones I have listed here are probably still mainly related to tampering detection.
Testing the new layout of paper title.
📖Paper, 👨💻Code, 📦Dataset, 🔗Other links, 📜News,
*Equal contribution. #Corresponding author.
Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
(EVP) Explicit Visual Prompting for Low-Level Structure Segmentations (CVPR '23) 📖, 👨💻 (including defocus blur, shadow, forgery, camouflaged dection)
Weihuang Liu1, Xi Shen2, Chi-Man Pun#,1, Xiaodong Cun#,2
1University of Macau 2Tencent AI Lab
SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device (ICCV '23) 📖, 👨💻
Weiran Gou∗1,2, Ziyao Yi∗1,2, Yan Xiang1,2, Shaoqing Li1,2, Zibin Liu1,2, Dehui Kong1,2, Ke Xu#1,2
1State Key Laboratory of Mobile Network and Mobile Multimedia Technology, 2Sanechips Technology, Chengdu, China
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision (ICLR '24_) 📖, 👨💻
Haoning Wu1*, Zicheng Zhang2*, Erli Zhang1*, Chaofeng Chen1, Liang Liao1, Annan Wang1, Chunyi Li2, Wenxiu Sun3, Qiong Yan3, Guangtao Zhai2, Weisi Lin1#
1Nanyang Technological University, 2Shanghai Jiaotong University, 3Sensetime Research
特征匹配,图像匹配问题。
目标检测,包括伪装物体目标检测和突出目标检测,COD以及SOD。
语义分割,将图片中完整语义(具有标签或者类别)的部分分割出来。不仅要进行目标检测检测到图像中的物体,还需要对每个像素分类。
异常检测,通常用于发现与正常模式或预期模式不符的图像与视频。
For the convenience of readers in checking the information of each paper, I have used different colors to mark the ranking of the conferences or journals where each paper was published on the CCF(China Computer Federation) Recommended List of International Conferences and Periodicals: A is marked in red, B in yellow, C in green, and sources not included are marked in grey. All articles are the result of the researchers' hard work, and the development and progress in the field of image tampering detection and localization cannot be separated from these outstanding researchers. "It should be pointed out that the recommended List by CCF for professionals and researchers on computing to publish their findings and results, is not sole criteria for academic evaluation, but as a suggestion or reference for the industry." ↩
99 commits
HTML
100.0%