一站式深伪检测与区块链存证平台，三模态识别伪造内容并防篡改。
DeepFake Forensics AI: A Multi-Modal Detection and Blockchain-Anchored Evidence Management Platform
- 多模态检测：图像、视频、音频分别用独立模型分析
- 检测准确率高：图像AUC达0.9868，生成架构识别准确率99.88%
- 证据链上存证：哈希+IPFS+以太坊智能合约保障不可篡改
AI生成的合成媒体泛滥，严重威胁司法与取证中的数字证据完整性。现有系统多仅处理单一模态，且缺乏防篡改证据保存机制。本文提出DeepFake Forensics AI，一个统一平台，可跨图像、视频、音频三模态检测合成内容，识别生成模型指纹，并将取证证据通过SHA-256哈希后存储于IPFS（Pinata），再以固件合约注册至以太坊区块链。系统训练四个独立神经网络：EfficientNet-B4图像检测器（AUC=0.9868）、双向LSTM视频检测器（AUC=0.9628）、ECAPA-TDNN音频检测器（错误等率18.63%），以及新型GAN指纹模块（准确率99.88%），可定位伪造图像背后的生成架构。平台提供基于React的前端与FastAPI后端，支持司法与取证流程部署。据我们所知，这是首个将多模态深伪检测与区块链证据链管理融合的系统。
原文摘要 · Abstract (English)
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.
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