ClaimTrace is a media forensics platform designed to combat misinformation and deepfakes. It utilizes a multimodal AI pipeline to cross-reference visual evidence against textual claims, detecting semantic inconsistencies, AI-generated content and digital tampering.
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Synthetic Media Detection: Implements Vision Transformer (ViT) to identify AI-generated imagery.
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Semantic Consistency Verification: leverages BLIP-2 (Vision-Language Model) to perform Zero-Shot Visual Question Answering (VQA). It "sees" the image context and mathematically compares it against the text caption using Natural Language Interface (NLI) cross-encoder.
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Digital Forensics (ELA): Performs Error Level Analysis to detect compression anomalies, highlighting regions where pixels have been manually altered or spliced.
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Hybrid Inference Engine: Features a device-agnostic architecture that automatically optimizes for 4-bit Quantization on NVIDIA GPUs or bfloat16 precision on CPUs, ensuring deployment flexibility across edge devices and cloud servers.
The system follows a microservices architecture, containerized for scalibility.
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Core: Python 3.11, PyTorch
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Models:
Salesforce/blip2-opt-2.7b(Vision-Language),cross-encoder/nli-distilroberta-base(Logic),umm-maybe/AI-image-detector(Synthetic Detection). -
API Framework: FastAPI
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Deployment: Docker
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Optimization:
bitsandbytes (4-bit Quantization),accelerate(Device mapping).
This application is fully containerized to resolve dependency conflicts.
1. Build the Container
docker build -t claimtrace .2. Run the Forensic Engine
docker run -p 8000:8000 claimtraceAccess the dashboard at http://localhost:8000 and the API documentation at http://localhost:8000/docs.
For development without Docker:
git clone https://github.com/ZohaibHassan16/ClaimTrace.git
cd ClaimTrace
python -m venv venv
source venv/bin/activate # Windows: .\venv\Scripts\activate
pip install -r requirements.txt # Use Python -m if on windows
python -m uvicorn app.main:app --reload