Face Deepfake-aware Recovery via Semantic-driven Facial Representation-based Watermarking

Existing image watermarking methods typically entangle visual content with spatial structure, making them highly sensitive to geometric transformations and alignment discrepancies, especially in the presence of deepfake manipulations. In this paper, we propose a semantic-driven facial watermarking framework for robust identity recovery. The key idea is to decouple identity-related semantic information from spatial layout and encode it into a compact and spatially robust semantic representation. Specifically, we decompose a face into semantic components and aggregate deep features into component-wise latent representations, which are quantized via independent codebooks and converted into a compact bitstream for embedding. After decoding, the embedded semantic information is recovered and used to reconstruct identity-consistent facial content, even under slight geometric distortions and tampering. Experiments on CelebA-HQ and FFHQ demonstrate that our method significantly outperforms existing watermarking approaches in terms of reconstruction quality, identity preservation, and retrieval accuracy under both photometric and geometric attacks, validating the effectiveness of semantic component-wise encoding for reliable face recovery.