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arXiv · 2509.24823

Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size

Benedetta Tondi·Andrea Costanzo·Mauro Barni
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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.

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Benedetta Tondi, Andrea Costanzo, Mauro Barni. 2025-09-29. Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size. https://arxiv.org/abs/2509.24823

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