8 accepted papers
Large Vision-Language Models (LVLMs) are vulnerable to jailbreak attacks that can generate harmful content. Existing detection methods are either limited to detecting specific attack types or are too time-consuming, making them impractical for real-world deployment. To address these challenges, we p…
In the burgeoning domain of machine learning, the reliance on third-party services for model training and the adoption of pre-trained models have surged. However, this reliance introduces vulnerabilities to model hijacking attacks, where adversaries manipulate models to perform unintended tasks, lea…
Deep neural networks (DNNs) are vulnerable to adversarial examples (AEs) that mislead the model while appearing benign to human observers. A critical concern is the transferability of AEs, which enables black-box attacks without direct access to the target model. However, many previous attacks have…
Text-to-image (T2I) models excel at generating high-quality images from text via powerful text encoders but training these encoders demands substantial computational resources. Consequently, many users seek pre-trained text encoders from model plugin-sharing platforms like Civitai and Hugging Face,…
Vertical Federated Learning (VFL) is a solution increasingly used by companies with the same user group but differing features, enabling them to collaboratively train a machine learning model. VFL ensures that clients exchange intermediate results extracted by their local models, without sharing ra…
The model extraction attack is an attack pattern aimed at stealing well-trained machine learning models' functionality or privacy information. With the gradual popularization of AI-related technologies in daily life, various well-trained models are being deployed. As a result, these models are consi…
Recently, unsupervised learning is proposed to avoid the performance degrading caused by synthesized paired computed tomography (CT) images. However, existing unsupervised methods for metal artifact reduction (MAR) only use features in image space, which is not enough to restore regions heavily corr…
In this paper, we propose an innovative approach, called Dense Nested Attention Network (DNANet), to directly restore a clear image from a hazy image with a new topology of connection paths. Firstly, through dense nested connections from inside to outside, the DNANet can fuse both shallow and deep f…