Lip-Focused Audio-Visual Consistency Detects Speech Content Manipulation beyond Coarse Synchronization

Abstract

Speech-content manipulation changes what a visible person appears to say by replacing the speech track and, in stronger attacks, regenerating mouth motion to match the forged utterance. Such attacks are difficult to expose with texture artifacts or coarse lip-sync scores because modern retalking models explicitly optimize audio-visual synchrony. We test the narrower hypothesis that speech replacement still perturbs the local relationship between acoustic dynamics and mouth motion after speaker identity or coarse synchrony is controlled. To isolate this signal, we introduce SyncSpeech, an LRS3-derived benchmark containing real videos, audio-splicing cheapfakes and Wav2Lip retalking attacks under same-speaker and different-speaker replacement. We then introduce C-MIND, a lip-focused detector that aligns audio and mouth-motion representations at frame level and models their agreement and disagreement without transcripts, automatic speech recognition features or an unmanipulated reference. On SyncSpeech, pretrained SyncLip-C achieves 95.813% scenario-averaged accuracy and 0.9943 AUC, outperforming sentence-level fusion, full-face inputs and a reproduced SyncNet baseline under the reported protocol. When trained on the corresponding FakeAVCeleb training split, C-MIND correctly classifies 278 of 280 samples in the selected unseen-speaker test subset, whereas accuracy falls to 82.775% on unseen Video-Retalking attacks. These results identify lip-focused speech-articulation consistency as a useful but generator-sensitive forensic signal within the evaluated settings.