Part-Level Self-Blending: Region Ground Truth by Construction for Deepfake Detection

Deepfake detectors produce a single real-or-fake score and therefore cannot indicate which part of a face was manipulated. We show that the score itself misses such edits. Each of eighteen published detectors is at or near chance when a manipulation is confined to a small facial region and at chance on a single eye (worse-eye AUC 0.48–0.55). We attribute the gap to the training signal, since self-blending, the most common method of synthesizing training fakes, blends the whole face and therefore provides no information about where the manipulation lies. We introduce Part-Level Self-Blending (P-SBI), which confines each synthetic blend to one named facial region, namely a single part, such as the left eye, or a small group of parts, such as the mouth, drawn from a fixed set of twelve that together cover every part of the face. Because the region is chosen at synthesis time, its mask is exact, and the same construction both trains a per-region prediction head and measures the region sensitivity of any detector. The verdict and the localization map need no anatomical input at test time, and only the per-region scores are read on a parser’s regions. With a DINOv3-H+ backbone adapted on under one percent of its weights, our detector, PARTAWARE, acquires a region sensitivity none of the eighteen has (coverage 0.847 over three seeds, against 0.587–0.676 for all eighteen) at no cost to the properties the field already measures, reaching crossdataset transfer 0.980 AUC and robustness to twenty-one corruption conditions (0.960 in distribution and 0.931 zero-shot, each at or above the strongest published detector. Removing P-SBI from the same backbone isolates the effect. Over three seeds, transfer falls only to 0.969 while coverage falls from 0.847 to 0.657, back into the published band. The lead also holds on diffusion-inpainted edits from two independent generators, whose region truth does not come from our construction.