74 citations · 101 across the 6 of their papers we have counts for
6 papers
ONRW: Optimizing inversion noise for high-quality and robust watermark
Xuan Ding, Xiu Yan, Chuanlong Xie +1
Watermarking methods have always been effective means of protecting intellectual property, yet they face significant challenges. Although existing deep learning-based watermarking…
Rethinking Out-of-Distribution Detection From a Human-Centric Perspective
Yao Zhu, Yuefeng Chen, Xiaodan Li +6
Out-Of-Distribution (OOD) detection has received broad attention over the years, aiming to ensure the reliability and safety of deep neural networks (DNNs) in real-world scenarios…
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
Yao Zhu, Yuefeng Chen, Xiaodan Li +6
Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and t…
Boosting Out-of-distribution Detection with Typical Features
Yao Zhu, YueFeng Chen, Chuanlong Xie +6
Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and safety of deep neural networks in real-world scenarios. Different from most previous OOD det…
Enhance the Visual Representation via Discrete Adversarial Training
Xiaofeng Mao, Yuefeng Chen, Ranjie Duan +6
Adversarial Training (AT), which is commonly accepted as one of the most effective approaches defending against adversarial examples, can largely harm the standard performance, thu…
Towards Understanding the Generative Capability of Adversarially Robust Classifiers
Yao Zhu, Jiacheng Ma, Jiacheng Sun +3
Recently, some works found an interesting phenomenon that adversarially robust classifiers can generate good images comparable to generative models. We investigate this phenomenon…