Related papers: Image Watermarking of Generative Diffusion Models

Image Watermarking of Generative Diffusion Models

URL: http://arxiv.org/abs/2502.10465v1
Date: Wed, 12 Feb 2025 09:00:48 GMT
Title: Image Watermarking of Generative Diffusion Models
Authors: Yunzhuo Chen, Jordan Vice, Naveed Akhtar, Nur Al Hasan Haldar, Ajmal Mian,
Abstract summary: 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.
Score: 42.982489491857145
License:
Abstract: Embedding watermarks into the output of generative models is essential for establishing copyright and verifiable ownership over the generated content. Emerging diffusion model watermarking methods either embed watermarks in the frequency domain or offer limited versatility of the watermark patterns in the image space, which allows simplistic detection and removal of the watermarks from the generated content. To address this issue, 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. The extractor forces the generator, during training, to effectively embed versatile, imperceptible watermarks in the generated content while simultaneously ensuring their precise recovery. 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.

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)
Spread them Apart: Towards Robust Watermarking of Generated Content [4.332441337407564]
We propose an approach to embed watermarks into the generated content to allow future detection of the generated content and identification of the user who generated it. We prove that watermarks embedded are guaranteed to be robust against additive perturbations of a bounded magnitude.
arXiv Detail & Related papers (2025-02-11T09:23:38Z)
RoboSignature: Robust Signature and Watermarking on Network Attacks [0.5461938536945723]
We present a novel adversarial fine-tuning attack that disrupts the model's ability to embed the intended watermark. Our findings emphasize the importance of anticipating and defending against potential vulnerabilities in generative systems.
arXiv Detail & Related papers (2024-12-22T04:36:27Z)
ROBIN: Robust and Invisible Watermarks for Diffusion Models with Adversarial Optimization [15.570148419846175]
Existing watermarking methods face the challenge of balancing robustness and concealment. This paper introduces a watermark hiding process to actively achieve concealment, thus allowing the embedding of stronger watermarks. Experiments on various diffusion models demonstrate the watermark remains verifiable even under significant image tampering.
arXiv Detail & Related papers (2024-11-06T12:14:23Z)
Image Watermarks are Removable Using Controllable Regeneration from Clean Noise [26.09012436917272]
A critical attribute of watermark techniques is their robustness against various manipulations. We introduce a watermark removal approach capable of effectively nullifying the state of the art watermarking techniques.
arXiv Detail & Related papers (2024-10-07T20:04:29Z)
On the Learnability of Watermarks for Language Models [80.97358663708592]
We ask whether language models can directly learn to generate watermarked text. We propose watermark distillation, which trains a student model to behave like a teacher model. We find that models can learn to generate watermarked text with high detectability.
arXiv Detail & Related papers (2023-12-07T17:41:44Z)
Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust [55.91987293510401]
Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. We introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed.
arXiv Detail & Related papers (2023-05-31T17:00:31Z)
Certified Neural Network Watermarks with Randomized Smoothing [64.86178395240469]
We propose a certifiable watermarking method for deep learning models. We show that our watermark is guaranteed to be unremovable unless the model parameters are changed by more than a certain l2 threshold. Our watermark is also empirically more robust compared to previous watermarking methods.
arXiv Detail & Related papers (2022-07-16T16:06:59Z)
Watermarking Images in Self-Supervised Latent Spaces [75.99287942537138]
We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time.
arXiv Detail & Related papers (2021-12-17T15:52:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.