Detection Limits and Statistical Separability of Tree Ring Watermarks in Rectified Flow-based Text-to-Image Generation Models
- URL: http://arxiv.org/abs/2504.03850v1
- Date: Fri, 04 Apr 2025 18:24:23 GMT
- Title: Detection Limits and Statistical Separability of Tree Ring Watermarks in Rectified Flow-based Text-to-Image Generation Models
- Authors: Ved Umrajkar, Aakash Kumar Singh,
- Abstract summary: Tree-Ring Watermarking is a significant technique for authenticating AI-generated images.<n>We evaluate and compare the detection and separability of watermarks between SD 2.1 and FLUX.1-dev models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Tree-Ring Watermarking is a significant technique for authenticating AI-generated images. However, its effectiveness in rectified flow-based models remains unexplored, particularly given the inherent challenges of these models with noise latent inversion. Through extensive experimentation, we evaluated and compared the detection and separability of watermarks between SD 2.1 and FLUX.1-dev models. By analyzing various text guidance configurations and augmentation attacks, we demonstrate how inversion limitations affect both watermark recovery and the statistical separation between watermarked and unwatermarked images. Our findings provide valuable insights into the current limitations of Tree-Ring Watermarking in the current SOTA models and highlight the critical need for improved inversion methods to achieve reliable watermark detection and separability. The official implementation, dataset release and all experimental results are available at this \href{https://github.com/dsgiitr/flux-watermarking}{\textbf{link}}.
Related papers
- Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal [57.84348166457113]
We introduce a novel feature adapting framework that leverages the representation capacity of a pre-trained image inpainting model.<n>Our approach bridges the knowledge gap between image inpainting and watermark removal by fusing information of the residual background content beneath watermarks into the inpainting backbone model.<n>For relieving the dependence on high-quality watermark masks, we introduce a new training paradigm by utilizing coarse watermark masks to guide the inference process.
arXiv Detail & Related papers (2025-04-07T02:37:14Z) - Safe-VAR: Safe Visual Autoregressive Model for Text-to-Image Generative Watermarking [18.251123923955397]
Autoregressive learning has become a dominant approach for text-to-image generation, offering high efficiency and visual quality.<n>Existing watermarking methods, designed for diffusion models, often struggle to adapt to the sequential nature of VAR models.<n>We propose Safe- VAR, the first watermarking framework specifically designed for autoregressive text-to-image generation.
arXiv Detail & Related papers (2025-03-14T11:45:10Z) - Dynamic watermarks in images generated by diffusion models [46.1135899490656]
High-fidelity text-to-image diffusion models have revolutionized visual content generation, but their widespread use raises significant ethical concerns.<n>We propose a novel multi-stage watermarking framework for diffusion models, designed to establish copyright and trace generated images back to their source.<n>Our work advances the field of AI-generated content security by providing a scalable solution for model ownership verification and misuse prevention.
arXiv Detail & Related papers (2025-02-13T03:23:17Z) - Robust Watermarks Leak: Channel-Aware Feature Extraction Enables Adversarial Watermark Manipulation [21.41643665626451]
We propose an attack framework that extracts leakage of watermark patterns using a pre-trained vision model.<n>Unlike prior works requiring massive data or detector access, our method achieves both forgery and detection evasion with a single watermarked image.<n>Our work exposes the robustness-stealthiness paradox: current "robust" watermarks sacrifice security for distortion resistance, providing insights for future watermark design.
arXiv Detail & Related papers (2025-02-10T12:55:08Z) - Trigger-Based Fragile Model Watermarking for Image Transformation Networks [2.38776871944507]
In fragile watermarking, a sensitive watermark is embedded in an object in a manner such that the watermark breaks upon tampering.
We introduce a novel, trigger-based fragile model watermarking system for image transformation/generation networks.
Our approach, distinct from robust watermarking, effectively verifies the model's source and integrity across various datasets and attacks.
arXiv Detail & Related papers (2024-09-28T19:34:55Z) - Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending [54.26862913139299]
We introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB)<n> TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models.<n>Experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy.
arXiv Detail & Related papers (2024-09-17T07:52:09Z) - TokenMark: A Modality-Agnostic Watermark for Pre-trained Transformers [67.57928750537185]
TokenMark is a robust, modality-agnostic, robust watermarking system for pre-trained models.
It embeds the watermark by fine-tuning the pre-trained model on a set of specifically permuted data samples.
It significantly improves the robustness, efficiency, and universality of model watermarking.
arXiv Detail & Related papers (2024-03-09T08:54:52Z) - 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) - WAVES: Benchmarking the Robustness of Image Watermarks [67.955140223443]
WAVES (Watermark Analysis Via Enhanced Stress-testing) is a benchmark for assessing image watermark robustness.
We integrate detection and identification tasks and establish a standardized evaluation protocol comprised of a diverse range of stress tests.
We envision WAVES as a toolkit for the future development of robust watermarks.
arXiv Detail & Related papers (2024-01-16T18:58:36Z) - T2IW: Joint Text to Image & Watermark Generation [74.20148555503127]
We introduce a novel task for the joint generation of text to image and watermark (T2IW)
This T2IW scheme ensures minimal damage to image quality when generating a compound image by forcing the semantic feature and the watermark signal to be compatible in pixels.
We demonstrate remarkable achievements in image quality, watermark invisibility, and watermark robustness, supported by our proposed set of evaluation metrics.
arXiv Detail & Related papers (2023-09-07T16:12:06Z)
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.