PatchSeal: A Robust and Intangible Image Watermarking Framework for AIGC

The rapid growth of artificial intelligence-generated content (AIGC) has created serious challenges for image copyright protection, since semantic edits and deep-fake manipulations can easily erase or distort embedded watermarks. Traditional robust watermarking methods, which are mainly designed to resist pixel-level distortions such as noise, compression or filtering, often fail when faced with content-level transformations generated by AIGC models. This paper presents PatchSeal, a robust and intangible image watermarking framework that combines multi-targeted and attention-oriented embedding with a focus-oriented masking. The proposed framework introduces a segmentation-assisted embedding strategy that distributes watermark bits across several prominent regions to improve resilience to semantic changes. An attention-based module, composed of a subject extraction branch and a channel weighting branch, adapts to the encoder towards texture-rich and semantically stable regions, improving both invisibility and robustness. Experiments conducted in three public object data sets show that PatchSeal achieves an average PSNR of 43.13 dB and a bit precision of 92.98 percent under various AIGC editing conditions, surpassing representative methods such as MBRS and FIN. These results demonstrate the effectiveness of the proposed method in resisting AIGC-driven manipulations and provide new practical paths and methodological insights for the design of robust watermarks in the AIGC era.