JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model Edits
- URL: http://arxiv.org/abs/2406.03720v1
- Date: Thu, 6 Jun 2024 03:31:41 GMT
- Title: JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model Edits
- Authors: Minzhou Pan, Yi Zeng, Xue Lin, Ning Yu, Cho-Jui Hsieh, Peter Henderson, Ruoxi Jia,
- Abstract summary: JIGMARK is a first-of-its-kind watermarking technique that enhances robustness through contrastive learning.
Our evaluation reveals that JIGMARK significantly surpasses existing watermarking solutions in resilience to diffusion-model edits.
- Score: 76.25962336540226
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Safe-SD: Safe and Traceable Stable Diffusion with Text Prompt Trigger for Invisible Generative Watermarking [20.320229647850017]
Stable diffusion (SD) models have typically flourished in the field of image synthesis and personalized editing.
The exposure of AI-created content on public platforms could raise both legal and ethical risks.
In this work, we propose a Safe and high-traceable Stable Diffusion framework (namely SafeSD) to adaptive implant the watermarks into the imperceptible structure.
arXiv Detail & Related papers (2024-07-18T05:53:17Z) - Diffusion-Based Hierarchical Image Steganography [60.69791384893602]
Hierarchical Image Steganography is a novel method that enhances the security and capacity of embedding multiple images into a single container.
It exploits the robustness of the Diffusion Model alongside the reversibility of the Flow Model.
The innovative structure can autonomously generate a container image, thereby securely and efficiently concealing multiple images and text.
arXiv Detail & Related papers (2024-05-19T11:29:52Z) - AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA [67.68750063537482]
Diffusion models have achieved remarkable success in generating high-quality images.
Recent works aim to let SD models output watermarked content for post-hoc forensics.
We propose textttmethod as the first implementation under this scenario.
arXiv Detail & Related papers (2024-05-18T01:25:47Z) - Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models [71.13610023354967]
Copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models.
We propose a diffusion model watermarking technique that is both performance-lossless and training-free.
arXiv Detail & Related papers (2024-04-07T13:30:10Z) - RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees [33.61946642460661]
This paper introduces a robust and agile watermark detection framework, dubbed as RAW.
We employ a classifier that is jointly trained with the watermark to detect the presence of the watermark.
We show that the framework provides provable guarantees regarding the false positive rate for misclassifying a watermarked image.
arXiv Detail & Related papers (2024-01-23T22:00:49Z) - Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs [23.639074918667625]
We propose a novel multi-bit box-free watermarking method for GANs with improved robustness against white-box attacks.
The watermark is embedded by adding an extra watermarking loss term during GAN training.
We show that the presence of the watermark has a negligible impact on the quality of the generated images.
arXiv Detail & Related papers (2023-10-25T18:38:10Z) - Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion
Models [63.20512617502273]
We propose a method called SDD to prevent problematic content generation in text-to-image diffusion models.
Our method eliminates a much greater proportion of harmful content from the generated images without degrading the overall image quality.
arXiv Detail & Related papers (2023-07-12T07:48:29Z) - DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing [94.24479528298252]
DragGAN is an interactive point-based image editing framework that achieves impressive editing results with pixel-level precision.
By harnessing large-scale pretrained diffusion models, we greatly enhance the applicability of interactive point-based editing on both real and diffusion-generated images.
We present a challenging benchmark dataset called DragBench to evaluate the performance of interactive point-based image editing methods.
arXiv Detail & Related papers (2023-06-26T06:04:09Z) - Intellectual Property Protection of Diffusion Models via the Watermark
Diffusion Process [22.38407658885059]
This paper introduces WDM, a novel watermarking solution for diffusion models without imprinting the watermark during task generation.
It involves training a model to concurrently learn a Watermark Diffusion Process (WDP) for embedding watermarks alongside the standard diffusion process for task generation.
arXiv Detail & Related papers (2023-06-06T06:31:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.