Explainable AI-Powered Multimodal Misinformation Detection Platform
Detect deepfakes, fake news, and manipulated media with transparency-backed AI verdicts
DeepShield is a production-ready, full-stack web platform designed to combat the rising threat of synthetic media. It accepts images, videos, news text, audio, and social media screenshots, subjecting them to a rigorous, multi-model forensic analysis.
Moving beyond simple "real or fake" probability scores, DeepShield provides human-readable LLM explanations, granular component breakdowns, temporal video analysis, and trusted-source cross-referencing.
It is built with a highly optimized decoupled architecture combining state-of-the-art Vision Transformers (ViT), BERT NLP classifiers, and Gemini-powered Explainable AI to create an end-to-end misinformation detection pipeline that doesn't just give a verdict — it shows you exactly why in plain, jargon-free English.
React.lazy, Suspense boundary code-splitting, and deferred 3D asset loading for an instant First Contentful Paint (FCP).DeepShield uses a robust, microservice-inspired architecture designed for high concurrency and stateless inference.
graph TD;
Client[React 18 + Vite SPA] -->|HTTPS / REST| API[FastAPI Gateway]
subgraph Backend Services
API --> Auth[JWT & Auth Service]
API --> Upload[SHA-256 Storage Cache]
API --> Inference[AI Inference Layer]
API --> DB[(Supabase PostgreSQL)]
end
subgraph AI Inference Engines
Inference --> Image[ViT + EfficientNet]
Inference --> Text[Multilingual BERT]
Inference --> Audio[Wav2Vec2]
Inference --> OCR[EasyOCR]
Inference --> LLM[Gemini 1.5 Flash]
end
Upload → SHA-256 Cache Check → ViT Classification → Generate Evidence Map (Grad-CAM++) → Tampering Check (ELA) → Metadata Trace → VLM Breakdown → VerdictUpload → Extract Frames → Analyze Individual Frames → Unnatural Movement Check → Audio Extraction & Deepfake Detection → Aggregate Signals → VerdictPaste Article → Scan for Sensationalism → NLP Classification → Fact-Checking (Trusted Sources) → Async LLM Summary → VerdictUpload → Text Extraction (EasyOCR) → NLP Credibility Scan → Layout Tampering Detection → Source Cross-Reference → VerdictUpload → Preprocess Audio → Extract Vocal Patterns → Detect Synthetic Voice Cloning → Analyze Acoustic Signals → VerdictDeepShield uses a standardized Deepfake Probability score alongside a 0–100 confidence scale, reinforced by a component breakdown.
| Score Range | Verdict | Indicator | Meaning |
|---|---|---|---|
| 81–100 | ✅ Very Likely Real | 🟢 Green | High confidence authentic |
| 61–80 | ✅ Likely Real | 🟢 Light Green | Probably authentic |
| 41–60 | ⚠️ Possibly Manipulated | 🟡 Amber | Uncertain — cross-check recommended |
| 21–40 | ❌ Likely Fake | 🟠 Orange | Probable manipulation detected |
| 0–20 | ❌ Very Likely Fake | 🔴 Red | Strong manipulation signals |
DeepShield is built to run locally for development and is fully containerized for deployment on platforms like Hugging Face Spaces.
git clone https://github.com/Spyderzz/DeepShield.git
cd DeepShield
cd backend
python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate
# Install dependencies
pip install torch==2.4.1 torchvision==0.19.1 --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt
Create a .env file in the root directory:
# Server
APP_HOST=0.0.0.0
APP_PORT=8000
DEBUG=true
CORS_ORIGINS=["http://localhost:5173", "https://your-huggingface-space.hf.space"]
# Database
DATABASE_URL=postgresql://postgres.xxx:[email protected]:6543/postgres
# AI Models & Explainability
LLM_PROVIDER=gemini
LLM_API_KEY=your_gemini_api_key_here
LLM_MODEL=gemini-2.0-flash
# Auth
JWT_SECRET_KEY=generate_a_secure_random_key
JWT_ALGORITHM=HS256
JWT_EXPIRATION_MINUTES=1440
cd backend
uvicorn main:app --reload --host 0.0.0.0 --port 8000
(API Documentation will be available at http://localhost:8000/docs)
cd frontend
npm install
npm run dev
(Open http://localhost:5173 to view the application)
slowapi limiters across endpoints to protect system resources and prevent API abuse.This project is licensed under the MIT License — see the LICENSE file for details.
Built with ❤️ using AI/ML for a safer digital world
Explainable AI-Powered Multimodal Misinformation Detection Platform
Detect deepfakes, fake news, and manipulated media with transparency-backed AI verdicts
DeepShield is a production-ready, full-stack web platform designed to combat the rising threat of synthetic media. It accepts images, videos, news text, audio, and social media screenshots, subjecting them to a rigorous, multi-model forensic analysis.
Moving beyond simple "real or fake" probability scores, DeepShield provides human-readable LLM explanations, granular component breakdowns, temporal video analysis, and trusted-source cross-referencing.
It is built with a highly optimized decoupled architecture combining state-of-the-art Vision Transformers (ViT), BERT NLP classifiers, and Gemini-powered Explainable AI to create an end-to-end misinformation detection pipeline that doesn't just give a verdict — it shows you exactly why in plain, jargon-free English.
React.lazy, Suspense boundary code-splitting, and deferred 3D asset loading for an instant First Contentful Paint (FCP).DeepShield uses a robust, microservice-inspired architecture designed for high concurrency and stateless inference.
graph TD;
Client[React 18 + Vite SPA] -->|HTTPS / REST| API[FastAPI Gateway]
subgraph Backend Services
API --> Auth[JWT & Auth Service]
API --> Upload[SHA-256 Storage Cache]
API --> Inference[AI Inference Layer]
API --> DB[(Supabase PostgreSQL)]
end
subgraph AI Inference Engines
Inference --> Image[ViT + EfficientNet]
Inference --> Text[Multilingual BERT]
Inference --> Audio[Wav2Vec2]
Inference --> OCR[EasyOCR]
Inference --> LLM[Gemini 1.5 Flash]
end
Upload → SHA-256 Cache Check → ViT Classification → Generate Evidence Map (Grad-CAM++) → Tampering Check (ELA) → Metadata Trace → VLM Breakdown → VerdictUpload → Extract Frames → Analyze Individual Frames → Unnatural Movement Check → Audio Extraction & Deepfake Detection → Aggregate Signals → VerdictPaste Article → Scan for Sensationalism → NLP Classification → Fact-Checking (Trusted Sources) → Async LLM Summary → VerdictUpload → Text Extraction (EasyOCR) → NLP Credibility Scan → Layout Tampering Detection → Source Cross-Reference → VerdictUpload → Preprocess Audio → Extract Vocal Patterns → Detect Synthetic Voice Cloning → Analyze Acoustic Signals → VerdictDeepShield uses a standardized Deepfake Probability score alongside a 0–100 confidence scale, reinforced by a component breakdown.
| Score Range | Verdict | Indicator | Meaning |
|---|---|---|---|
| 81–100 | ✅ Very Likely Real | 🟢 Green | High confidence authentic |
| 61–80 | ✅ Likely Real | 🟢 Light Green | Probably authentic |
| 41–60 | ⚠️ Possibly Manipulated | 🟡 Amber | Uncertain — cross-check recommended |
| 21–40 | ❌ Likely Fake | 🟠 Orange | Probable manipulation detected |
| 0–20 | ❌ Very Likely Fake | 🔴 Red | Strong manipulation signals |
DeepShield is built to run locally for development and is fully containerized for deployment on platforms like Hugging Face Spaces.
git clone https://github.com/Spyderzz/DeepShield.git
cd DeepShield
cd backend
python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate
# Install dependencies
pip install torch==2.4.1 torchvision==0.19.1 --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt
Create a .env file in the root directory:
# Server
APP_HOST=0.0.0.0
APP_PORT=8000
DEBUG=true
CORS_ORIGINS=["http://localhost:5173", "https://your-huggingface-space.hf.space"]
# Database
DATABASE_URL=postgresql://postgres.xxx:[email protected]:6543/postgres
# AI Models & Explainability
LLM_PROVIDER=gemini
LLM_API_KEY=your_gemini_api_key_here
LLM_MODEL=gemini-2.0-flash
# Auth
JWT_SECRET_KEY=generate_a_secure_random_key
JWT_ALGORITHM=HS256
JWT_EXPIRATION_MINUTES=1440
cd backend
uvicorn main:app --reload --host 0.0.0.0 --port 8000
(API Documentation will be available at http://localhost:8000/docs)
cd frontend
npm install
npm run dev
(Open http://localhost:5173 to view the application)
slowapi limiters across endpoints to protect system resources and prevent API abuse.This project is licensed under the MIT License — see the LICENSE file for details.
Built with ❤️ using AI/ML for a safer digital world