# OptMark: Robust Multi-bit Diffusion Watermarking via Inference Time Optimization

## TL;DR

OptMark introduces an inference time optimization method for embedding multi-bit watermarks into diffusion-generated images, enhancing robustness against various attacks while maintaining image quality and imperceptibility.

## Contribution

The paper presents a novel optimization-based multi-bit watermarking technique that embeds watermarks into diffusion model latents with improved robustness and reduced memory usage.

## Key findings

- Achieves invisible multi-bit watermarks with high robustness.
- Resists generative, geometric, and editing attacks effectively.
- Reduces memory consumption from linear to constant during optimization.

## Abstract

Watermarking diffusion-generated images is crucial for copyright protection and user tracking. However, current diffusion watermarking methods face significant limitations: zero-bit watermarking systems lack the capacity for large-scale user tracking, while multi-bit methods are highly sensitive to certain image transformations or generative attacks, resulting in a lack of comprehensive robustness. In this paper, we propose OptMark, an optimization-based approach that embeds a robust multi-bit watermark into the intermediate latents of the diffusion denoising process. OptMark strategically inserts a structural watermark early to resist generative attacks and a detail watermark late to withstand image transformations, with tailored regularization terms to preserve image quality and ensure imperceptibility. To address the challenge of memory consumption growing linearly with the number of denoising steps during optimization, OptMark incorporates adjoint gradient methods, reducing memory usage from O(N) to O(1). Experimental results demonstrate that OptMark achieves invisible multi-bit watermarking while ensuring robust resilience against valuemetric transformations, geometric transformations, editing, and regeneration attacks.