Semantic-Aware and Semi-Fragile Diffusion Watermarking for Proactive Deepfake Detection

The rapid progress of deepfake technology, which primarily manipulates facial identity and image semantics, has made detection and defense critically important. Conventional global watermarking methods offer limited capacity for protecting key semantic content, as they typically rely on uniformly distributed watermarks across the entire image. This letter presents a method that weave watermarks as intrinsic components into the semantic content of images (facial regions) in the latent space. By aligning watermark embedding regions with facial content, we establish an inherent fragility mechanism wherein any deepfake manipulation that modifies facial semantics inevitably disrupts the watermark, enabling precise detection. Simultaneously, adversarial training of the extractor ensures robustness against conventional signal processing operations. A local entropy perception module dynamically adjusts embedding intensity based on regional texture complexity, maintaining high perceptual fidelity. Extensive experiments indicate that compared to advanced methods, the proposed approach maintains robustness against conventional benign operations while achieving reliable detection of deepfake forgeries, thereby enabling precise protection of image semantic content.