{"ID":2863634,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.24823","arxiv_id":"2509.24823","title":"Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size","abstract":"We propose a high-payload image watermarking method for textual embedding, where a semantic description of the image - which may also correspond to the input text prompt-, is embedded inside the image. In order to be able to robustly embed high payloads in large-scale images - such as those produced by modern AI generators - the proposed approach builds upon a traditional watermarking scheme that exploits orthogonal and turbo codes for improved robustness, and integrates frequency-domain embedding and perceptual masking techniques to enhance watermark imperceptibility. Experiments show that the proposed method is extremely robust against a wide variety of image processing, and the embedded text can be retrieved also after traditional and AI inpainting, permitting to unveil the semantic modification the image has undergone via image-text mismatch analysis.","short_abstract":"We propose a high-payload image watermarking method for textual embedding, where a semantic description of the image - which may also correspond to the input text prompt-, is embedded inside the image. In order to be able to robustly embed high payloads in large-scale images - such as those produced by modern AI genera...","url_abs":"https://arxiv.org/abs/2509.24823","url_pdf":"https://arxiv.org/pdf/2509.24823v1","authors":"[\"Benedetta Tondi\",\"Andrea Costanzo\",\"Mauro Barni\"]","published":"2025-09-29T14:10:15Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\",\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}