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Method-Aware Prompting and Fusion — a dual-stream, CPU-quantized neural architecture designed for sub-second deepfake detection and transparent attribution.
Select any stage below to inspect mathematical formulations, CPU tensor shapes, and forensic heuristics.
Videos are decoded on CPU into 16 evenly spaced representative frames alongside 16kHz mono audio channels.
By uniformly sampling across temporal intervals rather than extracting every frame, CPU memory footprint is capped at under 900MB during active worker runs.
Compare multi-signal coherence between authentic speech and synthetic lip-syncing in real time.
| Axis | Score | Description |
|---|---|---|
| LipSync Coherence (S_AV) | 85% | Cross-modal temporal alignment between speaker phonemes and lip-motion visemes. |
| Spatial SRM Residuals | 88% | Spatial Rich Model 5x5 steganographic filtering detecting seam blending artifacts. |
| Spectral 2D-DCT Decay | 89% | Discrete Cosine Transform decomposition exposing synthetic periodic grid anomalies. |
| Facial Boundary Consistency | 95% | Edge gradient inspection detecting FaceSwap boundary interpolation inconsistencies. |
| Acoustic Synthesis Markers | 84% | Whisper Base auditory prompt conditioning uncovering neural voice-clone timbre artifacts. |
Phonemes extracted from Whisper align smoothly with visual lip visemes computed across 16 frames.