Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size
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| Format: | Preprint |
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2025
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| _version_ | 1866916976919576576 |
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| author | Tondi, Benedetta Costanzo, Andrea Barni, Mauro |
| author_facet | Tondi, Benedetta Costanzo, Andrea Barni, Mauro |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_24823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size Tondi, Benedetta Costanzo, Andrea Barni, Mauro Cryptography and Security Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size |
| topic | Cryptography and Security Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2509.24823 |