音频 Deepfake 检测

Audio-spoof and deepfake detection is an active benchmark area, including ASVspoof challenges. Detectors can exploit spectral, prosodic, and other learned cues, but performance may fall on unseen synthesis methods or real-world channels. The cited generalization study collected 37.9 hours of found recordings of celebrities and politicians, of which 17.2 hours were deepfakes, and reported weak generalization on that dataset. ASVspoof 2021 separately evaluated Logical Access, Physical Access, and a Deepfake task on manipulated, compressed speech posted online. These are dataset- and task-specific results, not universal performance claims. C2PA Content Credentials can attach provenance information when created and preserved, but do not prove factual truth or identify the human speaker. The cited generalization study reports that constant-Q-transform or log-spectrogram features improved detection performance relative to mel-spectrogram features in its controlled evaluation; this comparison is specific to that study. C2PA Content Credentials can provide signed provenance and edit-history information when present and valid. Credentials are optional, may be absent or stripped, and do not prove that media is true or fake.