FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
- URL: http://arxiv.org/abs/2410.20824v1
- Date: Mon, 28 Oct 2024 08:23:56 GMT
- Title: FreqMark: Invisible Image Watermarking via Frequency Based Optimization in Latent Space
- Authors: Yiyang Guo, Ruizhe Li, Mude Hui, Hanzhong Guo, Chen Zhang, Chuangjian Cai, Le Wan, Shangfei Wang,
- Abstract summary: Existing watermarking methods fall short in robustness against regeneration attacks.
FreqMark embeds the watermark by optimizing the latent frequency space of the images and then extracts the watermark through a pre-trained image encoder.
- Score: 16.022981250876942
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Invisible watermarking is essential for safeguarding digital content, enabling copyright protection and content authentication. However, existing watermarking methods fall short in robustness against regeneration attacks. In this paper, we propose a novel method called FreqMark that involves unconstrained optimization of the image latent frequency space obtained after VAE encoding. Specifically, FreqMark embeds the watermark by optimizing the latent frequency space of the images and then extracts the watermark through a pre-trained image encoder. This optimization allows a flexible trade-off between image quality with watermark robustness and effectively resists regeneration attacks. Experimental results demonstrate that FreqMark offers significant advantages in image quality and robustness, permits flexible selection of the encoding bit number, and achieves a bit accuracy exceeding 90% when encoding a 48-bit hidden message under various attack scenarios.
Related papers
- Latent-Mark: An Audio Watermark Robust to Neural Resynthesis [62.09761127079914]
Latent-Mark is the first zero-bit audio watermarking framework designed to survive semantic compression.<n>Our key insight is that robustness to the encode-decode process requires embedding the watermark within the invariant latent space.<n>Our work inspires future research into universal watermarking frameworks capable of maintaining integrity across increasingly complex and diverse generative distortions.
arXiv Detail & Related papers (2026-03-05T15:51:09Z) - ONRW: Optimizing inversion noise for high-quality and robust watermark [12.520973746455693]
We propose a high-quality and robust watermark framework based on the diffusion model.<n>We show that our method outperforms the stable signature method by an average of 10% across 12 different image transformations on COCO datasets.
arXiv Detail & Related papers (2026-01-24T09:22:29Z) - OptMark: Robust Multi-bit Diffusion Watermarking via Inference Time Optimization [66.69924980864053]
We propose OptMark, an optimization-based approach that embeds a robust multi-bit watermark into the intermediate latents of the diffusion denoising process.<n> OptMark strategically inserts a structural watermark early to resist generative attacks and a detail watermark late to withstand image transformations.<n> Experimental results demonstrate that OptMark achieves invisible multi-bit watermarking while ensuring robust resilience against valuemetric transformations, geometric transformations, editing, and regeneration attacks.
arXiv Detail & Related papers (2025-08-29T15:50:59Z) - IConMark: Robust Interpretable Concept-Based Watermark For AI Images [50.045011844765185]
We propose IConMark, a novel in-generation robust semantic watermarking method.<n>IConMark embeds interpretable concepts into AI-generated images, making it resilient to adversarial manipulation.<n>We demonstrate its superiority in terms of detection accuracy and maintaining image quality.
arXiv Detail & Related papers (2025-07-17T05:38:30Z) - TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivity [68.95168727940973]
Tamper-Aware Generative image WaterMarking method named TAG-WM.<n>This paper proposes a Tamper-Aware Generative image WaterMarking method named TAG-WM.
arXiv Detail & Related papers (2025-06-30T03:14:07Z) - Training-Free Watermarking for Autoregressive Image Generation [24.86897985016275]
IndexMark is a training-free watermarking framework for autoregressive image generation models.<n>We show IndexMark achieves state-of-the-art performance in terms of image quality and verification accuracy.
arXiv Detail & Related papers (2025-05-20T17:58:02Z) - ARIW-Framework: Adaptive Robust Iterative Watermarking Framework [14.782580487951018]
This paper proposes an adaptive robust iterative watermarking framework (ARIW-Framework)<n>It achieves high-quality watermarked images while maintaining exceptional robustness and generalization performance.
arXiv Detail & Related papers (2025-05-19T13:31:48Z) - Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking [53.434260110195446]
Safe-Sora is the first framework to embed graphical watermarks directly into the video generation process.<n>We develop a 3D wavelet transform-enhanced Mamba architecture with a adaptive localtemporal scanning strategy.<n>Experiments demonstrate Safe-Sora achieves state-of-the-art performance in terms of video quality, watermark fidelity, and robustness.
arXiv Detail & Related papers (2025-05-19T03:31:31Z) - The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields [77.76790894639036]
We propose NeRF Signature, a novel watermarking method for NeRF.
We employ a Codebook-aided Signature Embedding (CSE) that does not alter the model structure.
We also introduce a joint pose-patch encryption watermarking strategy to hide signatures into patches.
arXiv Detail & Related papers (2025-02-26T13:27:49Z) - SuperMark: Robust and Training-free Image Watermarking via Diffusion-based Super-Resolution [27.345134138673945]
We propose SuperMark, a robust, training-free watermarking framework.
SuperMark embeds the watermark into initial Gaussian noise using existing techniques.
It then applies pre-trained Super-Resolution models to denoise the watermarked noise, producing the final watermarked image.
For extraction, the process is reversed: the watermarked image is inverted back to the initial watermarked noise via DDIM Inversion, from which the embedded watermark is extracted.
Experiments demonstrate that SuperMark achieves fidelity comparable to existing methods while significantly improving robustness.
arXiv Detail & Related papers (2024-12-13T11:20:59Z) - InvisMark: Invisible and Robust Watermarking for AI-generated Image Provenance [10.161648213213828]
InvisMark is a novel watermarking technique designed for high-resolution AI-generated images.
InvisMark achieves state-of-the-art performance in imperceptibility.
We address potential vulnerabilities against advanced attacks and propose mitigation strategies.
arXiv Detail & Related papers (2024-11-10T16:22:22Z) - 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) - An undetectable watermark for generative image models [65.31658824274894]
We present the first undetectable watermarking scheme for generative image models.
In particular, an undetectable watermark does not degrade image quality under any efficiently computable metric.
Our scheme works by selecting the initial latents of a diffusion model using a pseudorandom error-correcting code.
arXiv Detail & Related papers (2024-10-09T18:33:06Z) - Certifiably Robust Image Watermark [57.546016845801134]
Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns.
Watermarking AI-generated content is a key technology to address these concerns.
We propose the first image watermarks with certified robustness guarantees against removal and forgery attacks.
arXiv Detail & Related papers (2024-07-04T17:56:04Z) - GumbelSoft: Diversified Language Model Watermarking via the GumbelMax-trick [50.35069175236422]
Large language models (LLMs) excellently generate human-like text, but also raise concerns about misuse in fake news and academic dishonesty.
Decoding-based watermark, particularly the GumbelMax-trick-based watermark(GM watermark), is a standout solution for safeguarding machine-generated texts.
We propose a new type of GM watermark, the Logits-Addition watermark, and its three variants, specifically designed to enhance diversity.
arXiv Detail & Related papers (2024-02-20T12:05:47Z) - 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) - TrustMark: Universal Watermarking for Arbitrary Resolution Images [21.74309490023683]
Imperceptible digital watermarking is important in copyright protection, misinformation prevention and responsible generative GAN.
We propose a GAN-based watermarking method with novel design in architecture and introduce TrustMark-RM - a watermark remover method.
Our methods achieve state-of-art performance on 3 benchmarks comprising arbitrary encoded images.
arXiv Detail & Related papers (2023-11-30T07:03:36Z) - FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models [64.89896692649589]
We propose FT-Shield, a watermarking solution tailored for the fine-tuning of text-to-image diffusion models.
FT-Shield addresses copyright protection challenges by designing new watermark generation and detection strategies.
arXiv Detail & Related papers (2023-10-03T19:50:08Z) - 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) - Supervised GAN Watermarking for Intellectual Property Protection [33.827150843939094]
We propose a watermarking method for Generative Adversarial Networks (GANs)
The aim is to watermark the GAN model so that any image generated by the GAN contains an invisible watermark (signature)
Results show that our method can effectively embed an invisible watermark inside the generated images.
arXiv Detail & Related papers (2022-09-07T20:52:05Z) - A Robust Document Image Watermarking Scheme using Deep Neural Network [10.938878993948517]
This paper proposes an end-to-end document image watermarking scheme using the deep neural network.
Specifically, an encoder and a decoder are designed to embed and extract the watermark.
A text-sensitive loss function is designed to limit the embedding modification on characters.
arXiv Detail & Related papers (2022-02-26T05:28:52Z) - 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.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.