  In this study, we investigate the vulnerability of image watermarks to
diffusion-model-based image editing, a challenge exacerbated by the
computational cost of accessing gradient information and the closed-source
nature of many diffusion models. To address this issue, we introduce JIGMARK.
This first-of-its-kind watermarking technique enhances robustness through
contrastive learning with pairs of images, processed and unprocessed by
diffusion models, without needing a direct backpropagation of the diffusion
process. Our evaluation reveals that JIGMARK significantly surpasses existing
watermarking solutions in resilience to diffusion-model edits, demonstrating a
True Positive Rate more than triple that of leading baselines at a 1% False
Positive Rate while preserving image quality. At the same time, it consistently
improves the robustness against other conventional perturbations (like JPEG,
blurring, etc.) and malicious watermark attacks over the state-of-the-art,
often by a large margin. Furthermore, we propose the Human Aligned Variation
(HAV) score, a new metric that surpasses traditional similarity measures in
quantifying the number of image derivatives from image editing.
