SyRC: Synthesis–Resampling Consistency for Deepfake Detection

Online forgery synthesis diversifies deepfake training data, yet detectors trained on these examples can remain sensitive to downsampling and restoration. To complement forgery diversity with stability under resampling, we introduce Synthesis–Resampling Consistency (SyRC). During training, SyRC refreshes self-blended forgeries online and constrains the feature discrepancy between each augmented instance and its resampled view. By sharing the underlying image and base augmentation draw, the two views isolate resampling-induced changes for the consistency constraint. The additional operations are confined to training, preserving single-view inference. With GenD trained on FaceForensics++, SyRC improves detection on four target datasets. The mean video-level area under the receiver operating characteristic curve (AUC) increases from $89.80\%$ to $93.59\%$. Matched comparisons also favor shared-base pairing over independent augmentation, supporting controlled within-instance consistency as a useful complement to synthetic forgery diversity.