提出可定位水印位置的图像水印检测方法，让水印藏在哪一目了然。
Where is the Watermark? Interpretable Watermark Detection at the Block Level
- 在离散小波域中分块嵌入水印，实现区域级可解释性。
- 对裁剪至原图一半仍保持鲁棒，对语义篡改敏感度高。
- 适合需要验证内容真实性的媒体平台与版权保护场景。
生成式AI的进步使得数字内容高度逼真，引发真实性、所有权和滥用问题。尽管水印成为追踪和保护数字媒体的重要手段，但现有图像水印方案多为黑箱，仅提供全局检测分数，无法揭示水印位置，削弱用户信任并难以判断篡改影响。本文提出一种后处理图像水印方法，结合局部嵌入与区域级可解释性。通过统计分块策略在离散小波变换域嵌入水印信号，生成检测图以揭示图像中可能被水印或篡改的区域。实验表明，该方法在常见图像变换下具有强鲁棒性，同时对语义操作敏感，且水印高度不可见。相比以往后处理方法，本方案提供更高可解释性，同时保持竞争力鲁棒性，例如对裁剪至原图一半仍有效。
原文摘要 · Abstract (English)
Recent advances in generative AI have enabled the creation of highly realistic digital content, raising concerns around authenticity, ownership, and misuse. While watermarking has become an increasingly important mechanism to trace and protect digital media, most existing image watermarking schemes operate as black boxes, producing global detection scores without offering any insight into how or where the watermark is present. This lack of transparency impacts user trust and makes it difficult to interpret the impact of tampering. In this paper, we present a post-hoc image watermarking method that combines localised embedding with region-level interpretability. Our approach embeds watermark signals in the discrete wavelet transform domain using a statistical block-wise strategy. This allows us to generate detection maps that reveal which regions of an image are likely watermarked or altered. We show that our method achieves strong robustness against common image transformations while remaining sensitive to semantic manipulations. At the same time, the watermark remains highly imperceptible. Compared to prior post-hoc methods, our approach offers more interpretable detection while retaining competitive robustness. For example, our watermarks are robust to cropping up to half the image.
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