145 citations · 205 across the 12 of their papers we have counts for
13 papers
Denoising-Aware Inversion: Revealing Privacy Risks in Noise-Protected Text Embeddings
Yubo Wang, Shujie Cui, James Bailey +5
Dense text embeddings are widely used in data mining, retrieval, and downstream machine learning systems due to their compact and semantically rich representations, but recent embe…
GeoDetect: Geometric Adversarial Detection for VLPs
Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie +2
Vision-language pre-trained models (VLPs) are widely used in real-world applications. However, they remain vulnerable to adversarial attacks. Although adversarial detection methods…
Whose Side Are You On? Investigating the Political Stance of Large Language Models
Pagnarasmey Pit, Xingjun Ma, Mike Conway +6
Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval. A…
Unlearnable Examples For Time Series
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +1
Unlearnable examples (UEs) refer to training samples modified to be unlearnable to Deep Neural Networks (DNNs). These examples are usually generated by adding error-minimizing nois…
End-to-End Anti-Backdoor Learning on Images and Time Series
Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani +2
Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security. These attacks manipulat…
Distilling Cognitive Backdoor Patterns within an Image
Hanxun Huang, Xingjun Ma, Sarah Erfani +1
This paper proposes a simple method to distill and detect backdoor patterns within an image: \emph{Cognitive Distillation} (CD). The idea is to extract the "minimal essence" from a…