Beyond Isolated Clips: Structured Learning for Temporal Forgery Localization

Temporal forgery localization must identify the extent of a manipulated interval, not only assign suspicious scores to short clips. We study speech-assisted visual localization in talking-person video, where the input is one audio-video clip and the output is a set of visual manipulation intervals on its original timeline. Our structured localizer scores a common graph of 25-Hz candidate segments, trains the scores through a globally normalized distribution over complete temporal labelings, and uses an input-derived speech prior only at inference. This separates the learning objective from the choice of speech-guided readout. On a locked 212-video ArEnAV preview, the resulting U+S system reaches 46.59/33.76/2.65 AP at IoU 0.50/0.75/0.95 for Diff2Lip-trained checkpoints and 34.03/23.91/2.62 for LatentSync-trained checkpoints. A source-calibrated independent model given the same decoder reaches 28.42/15.39/0.10 and 26.87/6.55/0.08, respectively; the AP75 gains are 18.37 and 17.36 percentage points. Cross-generator direction rows and a strict visual LAV-DF transfer evaluation provide complementary evidence, while a matched U-max control exposes a segment-versus-frame ranking trade-off. Together, these results support globally normalized segment learning as a practical complement to independent clip scoring under the fixed protocol, with each evaluation scope reported separately.