SyndromeFlow: Joint Temporal Alignment and Recomposition Inference for Audiovisual Watermark Authentication

Replacing or replaying watermarked segments can alter a video while leaving the embedded watermarks intact. Benign audio delays and clock drift make these edits harder to localize: tampering can bias alignment, and alignment errors can in turn create false tamper evidence. We propose SyndromeFlow, an audiovisual watermarking framework that jointly infers alignment and sparse tampering under a shared Bayesian posterior. Our code links segments through parity checks, so copying a segment with its watermark can break consistency with surrounding content. The receiver records failed checks as a syndrome and scores how candidate alignments and edited regions jointly explain it. Local tamper evidence thus refines alignment, while posterior averaging produces separate audio and video localization maps. Compared with a matched align-then-verify receiver using the same watermark, SyndromeFlow increases audio AUPRC from 0.566 to 0.637, reduces mean absolute alignment error from 5.6 ms to 0.9 ms, and maintains video AUPRC above 0.99. A controlled comparison of the inference steps attributes most of the improvement to tamper evidence revising alignment; averaging contributes further localization gains when alignment remains uncertain. In a complete-system comparison on the same speaking-person benchmark, each method embeds its own watermark before local copying. SyndromeFlow achieves video AUPRC of 0.994, compared with 0.086 for the strongest of five image-watermark baselines.