{"forensicMode":"builtin","engines":[{"id":"deepfakeshield-sidecar-audio","modality":"audio","role":"primary","kind":"neural","version":"unknown","selected":false,"available":false,"detail":"forensic sidecar could not be reached"},{"id":"deepfakeshield-acoustic-forensics","modality":"audio","role":"fallback","kind":"builtin","version":"1.1.0","selected":true,"available":true,"detail":"in-process deterministic acoustic engine"},{"id":"deepfakeshield-sidecar-video","modality":"video","role":"primary","kind":"neural","version":"unknown","selected":false,"available":false,"detail":"forensic sidecar could not be reached"},{"id":"deepfakeshield-wholeframe-forensics","modality":"video","role":"fallback","kind":"builtin","version":"1.1.0","selected":true,"available":true,"detail":"in-process deterministic whole-frame video engine"}],"registry":[{"key":"aasist-audio-antispoofing","displayName":"AASIST audio anti-spoofing","architecture":"AASIST (graph-attention audio anti-spoofing)","version":"2022.1","modality":"audio","source":"https://github.com/clovaai/aasist","license":{"name":"MIT (Copyright (c) 2021-present NAVER Corp.)","url":"https://github.com/clovaai/aasist/blob/main/LICENSE"},"weights":{"path":"aasist/AASIST.pth","sha256":null,"integrity":"not_applicable","checkedAt":"2026-10-17T13:13:48.573Z"},"status":"disabled","device":"cuda","expectedInput":{"channels":1,"encoding":"PCM s16le","sampleRate":16000,"windowSeconds":4.0375},"metrics":{"source":"not evaluated on this deployment"},"notes":"No weights at aasist/AASIST.pth. The channel is served by nothing until they are installed.","installedAt":"2026-10-17T09:22:21.715Z"},{"key":"builtin-acoustic-forensics","displayName":"Built-in acoustic forensics engine","architecture":"Deterministic LFCC / spectral / micro-prosody feature analysis with a versioned rule set","version":"1.1.0","modality":"audio","source":"DeepFakeShield, in-process","license":{"name":"Proprietary (part of DeepFakeShield)","url":null},"weights":{"path":null,"sha256":null,"integrity":"not_applicable","checkedAt":"2026-10-17T13:13:48.577Z"},"status":"active","device":"cpu","expectedInput":{"format":"f32le PCM","hopSize":160,"channels":1,"frameSize":512,"sampleRate":16000},"metrics":{"ruleSet":"audio-acoustic-v2","calibration":"not established"},"notes":"Always available. Produces reproducible, uncalibrated risk values from measured acoustic properties. Not equivalent to a trained anti-spoofing model; see the rule-set documentation.","installedAt":"2026-10-17T09:22:21.708Z"},{"key":"builtin-wholeframe-forensics","displayName":"Built-in whole-frame video forensics engine","architecture":"Deterministic residual / coding-grid / temporal-dispersion analysis with a versioned rule set","version":"1.1.0","modality":"video","source":"DeepFakeShield, in-process","license":{"name":"Proprietary (part of DeepFakeShield)","url":null},"weights":{"path":null,"sha256":null,"integrity":"not_applicable","checkedAt":"2026-10-17T13:13:48.579Z"},"status":"active","device":"cpu","expectedInput":{"frames":"PPM-decoded luma + RGB, 384×384, sampled at the profile frame rate","windowFrames":8},"metrics":{"ruleSet":"video-wholeframe-v3","calibration":"not established","faceLocalisation":"none"},"notes":"Always available. Whole-frame analysis: performs no face localisation, so manipulation confined to a small region contributes less evidence than under a face-region detector.","installedAt":"2026-10-17T09:22:21.712Z"},{"key":"retinaface-face-detector","displayName":"RetinaFace face detector","architecture":"RetinaFace (MobileNet 0.25 backbone)","version":"1.0","modality":"video","source":"https://github.com/deepinsight/insightface","license":{"name":"MIT","url":"https://github.com/deepinsight/insightface/blob/master/LICENSE"},"weights":{"path":null,"sha256":null,"integrity":"not_applicable","checkedAt":"2026-10-17T13:13:48.580Z"},"status":"active","device":"cpu","expectedInput":{"size":640,"purpose":"localisation","facesPerFrame":10},"metrics":{"source":"localisation component; not a detector on its own"},"notes":"Locates faces so that manipulation confined to a face region can be measured against that region rather than the whole frame. Optional: the sidecar falls back to the OpenCV cascade, which needs no download, and reports which localiser it used on every result. Weights are not installed at this host: this channel is unavailable until they are, and nothing is substituted for it.","installedAt":"2026-10-17T09:22:21.726Z"},{"key":"xception-face-manipulation","displayName":"Xception face-manipulation classifier","architecture":"Xception (ImageNet pretrained, fine-tuned on face manipulation)","version":"1.0","modality":"video","source":"https://github.com/ondyari/FaceForensics","license":{"name":"Research use — see upstream repository","url":"https://github.com/ondyari/FaceForensics/blob/master/LICENSE"},"weights":{"path":"xception/ffpp_c23.pth","sha256":null,"integrity":"not_applicable","checkedAt":"2026-10-17T13:13:48.582Z"},"status":"disabled","device":"cuda","expectedInput":{"size":299,"channels":3,"detector":"retinaface","facesPerFrame":1},"metrics":{"source":"not evaluated on this deployment"},"notes":"No weights at xception/ffpp_c23.pth. The channel is served by nothing until they are installed.","installedAt":"2026-10-17T09:22:21.718Z"}]}