Where is the Watermark? Interpretable Watermark Detection at the Block Level

TL;DR

This paper introduces an interpretable, block-level watermark detection method that localizes watermarked regions in images, enhancing transparency and robustness against manipulations while maintaining imperceptibility.

Contribution

The authors propose a post-hoc watermarking technique that embeds signals in the wavelet domain with region-level interpretability, improving transparency over existing black-box methods.

Findings

Achieves strong robustness against common image transformations.

Provides interpretable detection maps indicating watermarked regions.

Remains sensitive to semantic manipulations and is highly imperceptible.

Abstract

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.