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This paper introduces DeepFake Forensics AI, a platform for multi-modal deepfake detection across image, video, and audio, coupled with blockchain-anchored evidence management. The system employs modality-specific neural networks, including EfficientNet-B4 for images, Bidirectional LSTM for video, ECAPA-TDNN for audio, and a novel GAN fingerprinting module, achieving high detection accuracy. Forensic evidence is secured using SHA-256 hashing, IPFS storage, and an Ethereum smart contract, ensuring tamper-proof chain-of-custody.
Securing forensic evidence against deepfakes just got easier: this platform combines multi-modal detection with blockchain immutability.
The proliferation of AI-generated synthetic media poses a critical threat to the integrity of digital evidence in legal and forensic contexts. Existing deepfake detection systems typically address a single modality and provide no mechanism for tamper-proof evidence preservation. We present DeepFake Forensics AI, a unified platform that detects synthetic media across image, video, and audio modalities, identifies generative architecture fingerprints, and anchors forensic evidence immutably on the Ethereum blockchain. Our system trains four independent neural networks from scratch: an EfficientNet-B4 image detector (AUC = 0.9868), a Bidirectional LSTM video detector (AUC= 0.9628), an ECAPA-TDNN audio detector (EER = 18.63%), and a novel GAN fingerprinting module (accuracy = 99.88%) that identifies the generative architecture behind a fake image. Evidence files are hashed with SHA-256, stored on IPFS via Pinata, and registered on-chain via a Solidity smart contract with role-based access control. The platform provides a React frontend and FastAPI backend suitable for deployment in forensic and legal workflows. To our knowledge, this is the first system to unify multi-modal deepfake detection with blockchain-based chain-of custody management.