将图像语义文本嵌入图像，支持大尺寸生成图的高容量水印。
Of-SemWat: High-payload text embedding for semantic watermarking of AI-generated images with arbitrary size
- 利用正交码与涡轮码提升水印鲁棒性，结合频域嵌入和感知掩蔽。
- 在多种图像处理下仍可准确提取水印文本，包括传统与AI修复。
- 适合需要验证生成图像真实性的研究者或平台方使用。
我们提出一种高容量图像水印方法，将图像的语义描述（可能对应输入文本提示）嵌入图像内部。为在大规模图像（如现代AI生成图像）中稳健嵌入高容量信息，该方法基于传统水印框架，采用正交码与涡轮码增强鲁棒性，并融合频域嵌入与感知掩蔽技术以提升不可察觉性。实验表明，该方法对多种图像处理操作均具有极强鲁棒性，即使在传统修复与AI图像修复后仍可成功提取嵌入文本，从而通过图像-文本不匹配分析揭示图像被修改的语义变化。
原文摘要 · Abstract (English)
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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