AI-generated image watermarks can be easily removed, say researchers

Now that AI can make fake images that look real, how can we know what's legitimate and what isn't? One of the primary ways has been the use of defensive watermarking, which means embedding invisible markers in AI-generated images to show they were made up. Now, researchers have broken that...

Removing Box-Free Watermarks for Image-To-Image Models Via Query-Based Reverse Engineering

The intellectual property of deep generative networks GNets can be protected using a cascaded hiding network HNet which embeds watermarks or marks into GNet outputs, known as box-free watermarking. Although both GNet and HNet are encapsulated in a black box called operation network, or ONet, with...

Hashed Watermark As a Filter: Defeating Forging and Overwriting Attacks in Weight-Based Neural Network Watermarking

As valuable digital assets, deep neural networks necessitate robust ownership protection, positioning neural network watermarking NNW as a promising solution. Among various NNW approaches, weight-based methods are favored for their simplicity and practicality; however, they remain vulnerable to...

WordPress Pro Bulk Watermark Plugin for WordPress <= 2.0 - Path Traversal Vulnerability

Path Traversal Vulnerability discovered by Tran Nguyen Bao Khanh VCI - VNPT Cyber Immunity in WordPress Theme Pro Bulk Watermark Plugin for WordPress versions = 2.0...

WordPress Pro Bulk Watermark Plugin for WordPress Theme <= 2.0 is vulnerable to Path Traversal

Software Pro Bulk Watermark Plugin for WordPress Type Theme Vulnerable versions = 2.0 Fixed in N/A OWASP Top 10 A3: Injection Classification Path Traversal CVE CVE-2025-28973 Patch priority High CVSS severity High 6.5 Developer Claim ownership PSID c40f943bba08 Credits Tran Nguyen Bao Khanh VCI -...

Invariant-Based Robust Weights Watermark for Large Language Models

Watermarking technology has gained significant attention due to the increasing importance of intellectual property IP rights, particularly with the growing deployment of large language models LLMs on billions resource-constrained edge devices. To counter the potential threats of IP theft by...

Mitigating Watermark Stealing Attacks in Generative Models Via Multi-Key Watermarking

Watermarking offers a promising solution for GenAI providers to establish the provenance of their generated content. A watermark is a hidden signal embedded in the generated content, whose presence can later be verified using a secret watermarking key. A threat to GenAI providers are \emphwaterma...

Enhancing LLM Watermark Resilience against Both Scrubbing and Spoofing Attacks

Watermarking is a promising defense against the misuse of large language models LLMs, yet it remains vulnerable to scrubbing and spoofing attacks. This vulnerability stems from an inherent trade-off governed by watermark window size: smaller windows resist scrubbing better but are easier to...

When There Is No Decoder: Removing Watermarks from Stable Diffusion Models in a No-Box Setting

Watermarking has emerged as a promising solution to counter harmful or deceptive AI-generated content by embedding hidden identifiers that trace content origins. However, the robustness of current watermarking techniques is still largely unexplored, raising critical questions about their...

Watermarking Quantum Neural Networks Based on Sample Grouped and Paired Training

Quantum neural networks QNNs leverage quantum computing to create powerful and efficient artificial intelligence models capable of solving complex problems significantly faster than traditional computers. With the fast development of quantum hardware technology, such as superconducting qubits,...

LexiMark: Robust Watermarking via Lexical Substitutions to Enhance Membership Verification of an LLM's Textual Training Data

Large language models LLMs can be trained or fine-tuned on data obtained without the owner's consent. Verifying whether a specific LLM was trained on particular data instances or an entire dataset is extremely challenging. Dataset watermarking addresses this by embedding identifiable modification...

CertDW: Towards Certified Dataset Ownership Verification via Conformal Prediction

Deep neural networks DNNs rely heavily on high-quality open-source datasets e.g., ImageNet for their success, making dataset ownership verification DOV crucial for protecting public dataset copyrights. In this paper, we find existing DOV methods implicitly assume that the verification process is...

Watermarking Autoregressive Image Generation

Watermarking the outputs of generative models has emerged as a promising approach for tracking their provenance. Despite significant interest in autoregressive image generation models and their potential for misuse, no prior work has attempted to watermark their outputs at the token level. In thi...

KGMark: a Diffusion Watermark for Knowledge Graphs

Knowledge graphs KGs are ubiquitous in numerous real-world applications, and watermarking facilitates protecting intellectual property and preventing potential harm from AI-generated content. Existing watermarking methods mainly focus on static plain text or image data, while they can hardly be...

GaussMarker: Robust Dual-Domain Watermark for Diffusion Models

As Diffusion Models DM generate increasingly realistic images, related issues such as copyright and misuse have become a growing concern. Watermarking is one of the promising solutions. Existing methods inject the watermark into the single-domain of initial Gaussian noise for generation, which...

A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarks

We present a novel attack specifically designed against Tree-Ring, a watermarking technique for diffusion models known for its high imperceptibility and robustness against removal attacks. Unlike previous removal attacks, which rely on strong assumptions about attacker capabilities, our attack on...

TimeWak: Temporal Chained-Hashing Watermark for Time Series Data

Synthetic time series generated by diffusion models enable sharing privacy-sensitive datasets, such as patients' functional MRI records. Key criteria for synthetic data include high data utility and traceability to verify the data source. Recent watermarking methods embed in homogeneous latent...

HeavyWater and SimplexWater: Watermarking Low-Entropy Text Distributions

Large language model LLM watermarks enable authentication of text provenance, curb misuse of machine-generated text, and promote trust in AI systems. Current watermarks operate by changing the next-token predictions output by an LLM. The updated i.e., watermarked predictions depend on random side...

StealthInk: a Multi-Bit and Stealthy Watermark for Large Language Models

Watermarking for large language models LLMs offers a promising approach to identifying AI-generated text. Existing approaches, however, either compromise the distribution of original generated text by LLMs or are limited to embedding zero-bit information that only allows for watermark detection b...

Video Signature: In-Generation Watermarking for Latent Video Diffusion Models

The rapid development of Artificial Intelligence Generated Content AIGC has led to significant progress in video generation but also raises serious concerns about intellectual property protection and reliable content tracing. Watermarking is a widely adopted solution to this issue, but existing...