Trigger-Based Fragile Model Watermarking for Image Transformation Networks
- URL: http://arxiv.org/abs/2409.19442v1
- Date: Sat, 28 Sep 2024 19:34:55 GMT
- Title: Trigger-Based Fragile Model Watermarking for Image Transformation Networks
- Authors: Preston K. Robinette, Dung T. Nguyen, Samuel Sasaki, Taylor T. Johnson,
- Abstract summary: 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.
- Score: 2.38776871944507
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In fragile watermarking, a sensitive watermark is embedded in an object in a manner such that the watermark breaks upon tampering. This fragile process can be used to ensure the integrity and source of watermarked objects. While fragile watermarking for model integrity has been studied in classification models, image transformation/generation models have yet to be explored. We introduce a novel, trigger-based fragile model watermarking system for image transformation/generation networks that takes advantage of properties inherent to image outputs. For example, manifesting watermarks as specific visual patterns, styles, or anomalies in the generated content when particular trigger inputs are used. Our approach, distinct from robust watermarking, effectively verifies the model's source and integrity across various datasets and attacks, outperforming baselines by 94%. We conduct additional experiments to analyze the security of this approach, the flexibility of the trigger and resulting watermark, and the sensitivity of the watermarking loss on performance. We also demonstrate the applicability of this approach on two different tasks (1 immediate task and 1 downstream task). This is the first work to consider fragile model watermarking for image transformation/generation networks.
Related papers
- 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.
We propose a novel multi-stage watermarking framework for diffusion models, designed to establish copyright and trace generated images back to their source.
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) - Image Watermarking of Generative Diffusion Models [42.982489491857145]
We propose a watermarking technique that embeds watermark features into the diffusion model itself.
Our technique enables training of a paired watermark extractor for a generative model that is learned through an end-to-end process.
We demonstrate highly accurate watermark embedding/detection and show that it is also possible to distinguish between different watermarks embedded with our method to differentiate between generative models.
arXiv Detail & Related papers (2025-02-12T09:00:48Z) - 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.
Unlike prior works requiring massive data or detector access, our method achieves both forgery and detection evasion with a single watermarked image.
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) - 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) - A Watermark-Conditioned Diffusion Model for IP Protection [31.969286898467985]
We propose a unified watermarking framework for content copyright protection within the context of diffusion models.
To tackle this challenge, we propose a Watermark-conditioned Diffusion model called WaDiff.
Our method is effective and robust in both the detection and owner identification tasks.
arXiv Detail & Related papers (2024-03-16T11:08:15Z) - 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) - 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) - Safe and Robust Watermark Injection with a Single OoD Image [90.71804273115585]
Training a high-performance deep neural network requires large amounts of data and computational resources.
We propose a safe and robust backdoor-based watermark injection technique.
We induce random perturbation of model parameters during watermark injection to defend against common watermark removal attacks.
arXiv Detail & Related papers (2023-09-04T19:58:35Z) - Exploring Structure Consistency for Deep Model Watermarking [122.38456787761497]
The intellectual property (IP) of Deep neural networks (DNNs) can be easily stolen'' by surrogate model attack.
We propose a new watermarking methodology, namely structure consistency'', based on which a new deep structure-aligned model watermarking algorithm is designed.
arXiv Detail & Related papers (2021-08-05T04:27:15Z) - Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal
Attack for DNN Models [72.9364216776529]
We propose a novel watermark removal attack from a different perspective.
We design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations.
Our attack can bypass state-of-the-art watermarking solutions with very high success rates.
arXiv Detail & Related papers (2020-09-18T09:14:54Z)
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.