SeLo-Mark: Robust Localized Watermarking with Content-Derived Semantic Codes for Facial Integrity Verification

Image watermarking enables proactive facial forensics by embedding information before publication. Verifying facial content, however, requires more than recovering a payload: the embedded signal must represent the original face while remaining recoverable after image processing. Random payloads provide no such content reference, while geometric transformations disrupt the spatial correspondence required by localized watermarking. We propose SeLo-Mark, a content-derived localized watermarking framework for facial integrity verification. First, a compact semantic reference is derived from the protected face and embedded only in the surrounding unprotected region, leaving the protected facial pixels unchanged. Second, a shared geometric estimate is used to align both watermark recovery and facial-content encoding after geometric processing, so that the recovered reference can be compared with the received face in a common coordinate frame. The semantic reference is obtained by projecting frozen CLIP features with PCA and binarizing them into a 32-bit facial-content code, which is combined with an independent 16-bit tracing field. At reception, the payload is recovered by a reader adapted to rectified images, and facial consistency is assessed by comparing the recovered content code with one recomputed from the received face, without reconstructing the original face. Across four editor families and 22 processing conditions on 2,999 CelebA-HQ images, SeLo-Mark achieves 99.23% AUC and 95.85% F1. On unedited watermarked images, exact 48-bit recovery is increased from 88.38% with direct, unadapted decoding to 99.82% with the full receiver.