1 citations · 2 across the 24 of their papers we have counts for
6 papers · 2 filters
AEIOU: A Unified Defense Framework against NSFW Prompts in Text-to-Image Models
Yiming Wang, Jiahao Chen, Qingming Li +4
As text-to-image (T2I) models advance and gain widespread adoption, their associated safety concerns are becoming increasingly critical. Malicious users exploit these models to gen…
CAMH: Advancing Model Hijacking Attack in Machine Learning
Xing He, Jiahao Chen, Yuwen Pu +5
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 int…
Enhancing Adversarial Transferability with Adversarial Weight Tuning
Jiahao Chen, Zhou Feng, Rui Zeng +6
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…
Rethinking the Vulnerabilities of Face Recognition Systems:From a Practical Perspective
Jiahao Chen, Zhiqiang Shen, Yuwen Pu +5
Face Recognition Systems (FRS) have increasingly integrated into critical applications, including surveillance and user authentication, highlighting their pivotal role in modern se…
Dullahan: Stealthy Backdoor Attack against Without-Label-Sharing Split Learning
Yuwen Pu, Zhuoyuan Ding, Jiahao Chen +4
As a novel privacy-preserving paradigm aimed at reducing client computational costs and achieving data utility, split learning has garnered extensive attention and proliferated wid…
Mellivora Capensis: A Backdoor-Free Training Framework on the Poisoned Dataset without Auxiliary Data
Yuwen Pu, Jiahao Chen, Chunyi Zhou +4
The efficacy of deep learning models is profoundly influenced by the quality of their training data. Given the considerations of data diversity, data scale, and annotation expenses…