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...

ME: Trigger Element Combination Backdoor Attack on Copyright Infringement

The capability of generative diffusion models DMs like Stable Diffusion SD in replicating training data could be taken advantage of by attackers to launch the Copyright Infringement Attack, with duplicated poisoned image-text pairs. SilentBadDiffusion SBD is a method proposed recently, which shew...

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...

DiffUMI: Training-Free Universal Model Inversion Via Unconditional Diffusion for Face Recognition

Face recognition technology presents serious privacy risks due to its reliance on sensitive and immutable biometric data. To address these concerns, such systems typically convert raw facial images into embeddings, which are traditionally viewed as privacy-preserving. However, model inversion...

SAGE: Exploring the Boundaries of Unsafe Concept Domain with Semantic-Augment Erasing

Diffusion models DMs have achieved significant progress in text-to-image generation. However, the inevitable inclusion of sensitive information during pre-training poses safety risks, such as unsafe content generation and copyright infringement. Concept erasing finetunes weights to unlearn...

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...

Optimization-Free Universal Watermark Forgery with Regenerative Diffusion Models

Watermarking becomes one of the pivotal solutions to trace and verify the origin of synthetic images generated by artificial intelligence models, but it is not free of risks. Recent studies demonstrate the capability to forge watermarks from a target image onto cover images via adversarial...

Silence Is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-Based Talking-Head Generation

Advances in talking-head animation based on Latent Diffusion Models LDM enable the creation of highly realistic, synchronized videos. These fabricated videos are indistinguishable from real ones, increasing the risk of potential misuse for scams, political manipulation, and misinformation. Hence,...

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...

Unveiling Impact of Frequency Components on Membership Inference Attacks for Diffusion Models

Diffusion models have achieved tremendous success in image generation, but they also raise significant concerns regarding privacy and copyright issues. Membership Inference Attacks MIAs are designed to ascertain whether specific data were utilized during a model's training phase. As current MIAs...

MixBridge: Heterogeneous Image-To-Image Backdoor Attack through Mixture of Schrödinger Bridges

This paper focuses on implanting multiple heterogeneous backdoor triggers in bridge-based diffusion models designed for complex and arbitrary input distributions. Existing backdoor formulations mainly address single-attack scenarios and are limited to Gaussian noise input models. To fill this gap...

Structure Disruption: Subverting Malicious Diffusion-Based Inpainting Via Self-Attention Query Perturbation

The rapid advancement of diffusion models has enhanced their image inpainting and editing capabilities but also introduced significant societal risks. Adversaries can exploit user images from social media to generate misleading or harmful content. While adversarial perturbations can disrupt...

CVE-2024-32024

Kohyass is a GUI for Kohya's Stable Diffusion trainers. Kohyass is vulnerable to a path injection in the commongui.py addprepostfix function. This vulnerability is fixed in 23.1.5...

CVE-2024-32023

Kohyass is a GUI for Kohya's Stable Diffusion trainers. Kohyass is vulnerable to a path injection in the commongui.py findandreplace function. This vulnerability is fixed in 23.1.5...

CVE-2024-31462

stable-diffusion-webui is a web interface for Stable Diffusion, implemented using Gradio library. Stable-diffusion-webui 1.7.0 is vulnerable to a limited file write affecting Windows systems. The createui method Backup/Restore tab in modules/uiextensions.py takes user input into the configsavenam...

Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems

Autonomous driving systems ADS increasingly rely on deep learning-based perception models, which remain vulnerable to adversarial attacks. In this paper, we revisit adversarial attacks and defense methods, focusing on road sign recognition and lead object detection and prediction e.g., relative...

PT-2025-22503

Name of the Vulnerable Software and Affected Versions fc-stable-diffusion-plus version 1.0.18 Description The issue is related to insecure permissions, which can allow attackers to escalate privileges and compromise the customer cloud account. Recommendations For fc-stable-diffusion-plus version...

fc-stable-diffusion 安全漏洞

fc-stable-diffusion is an open source tool from Serverless Devs Registry for deploying stable-diffusion to AliCloud Functional Computing. A security vulnerability exists in fc-stable-diffusion v1.0.18, which stems from improper privileges and could lead to elevated privileges and customer cloud...

CVE-2025-45468

The open-source tool fc-stable-diffusion (and specifically fc-stable-diffusion-plus version 1.0.18 ) is vulnerable to a privilege escalation flaw caused by incorrect permission assignment for critical resources. This vulnerability allows an attacker to escalate privileges and potentially compromi...