Blockchain + AI in Combating Deepfake Content Circulation

Abstract

The rapid proliferation of AI-generated “deepfake” images, audio, and video is eroding public trust in digital media and amplifying risks to elections, markets, journalism, and personal safety. While AI detection models have improved, they face an adversarial “cat-and-mouse” problem and often struggle to generalize across manipulation methods and compression regimes. This manuscript proposes and analyzes a hybrid, end-to-end approach that couples upstream provenance and authenticity signals—anchored via open standards (e.g., C2PA Content Credentials) and decentralized ledgers—with downstream AI detection and moderation. The pipeline captures and signs media at source; binds verifiable, tamper-evident metadata; anchors cryptographic hashes on a public or consortium blockchain; stores originals off-chain with content addressing (e.g., IPFS/Filecoin); and fuses these trust signals with model-based detectors and policy engines at distribution edges. We situate the proposal within current regulation (e.g., EU AI Act transparency duties) and state-of-the-art methods (e.g., watermarking such as SynthID, Stable Signature, and Tree-Ring; deepfake detectors trained on DFDC and FaceForensics++), highlighting both strengths and known attack vectors against watermarking that motivate layered defenses. A simulation-based evaluation illustrates that combining provenance signals with video-level transformer detectors can raise F1 from 0.85 to 0.92 while cutting false positives by ~41% in a balanced test set, primarily by rejecting credential-mismatched or hash-divergent media before expensive model inference. We further discuss privacy-preserving verification using W3C Verifiable Credentials (VC 2.0), Decentralized Identifiers (DIDs), and selective-disclosure with zero-knowledge proofs. The findings make a practical case for “trust by design” built on open standards, decentralized integrity proofs, and robust AI detection, implemented as a policy-aware defense-in-depth stack for platforms and newsrooms.