no code implementations • 28 Mar 2017 • Nan Zhang, Soteris Demetriou, Xianghang Mi, Wenrui Diao, Kan Yuan, Peiyuan Zong, Feng Qian, Xiao-Feng Wang, Kai Chen, Yuan Tian, Carl A. Gunter, Kehuan Zhang, Patrick Tague, Yue-Hsun Lin
We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas.
Cryptography and Security
no code implementations • 5 Jan 2018 • Shuaike Dong, Menghao Li, Wenrui Diao, Xiangyu Liu, Jian Liu, Zhou Li, Fenghao Xu, Kai Chen, Xiao-Feng Wang, Kehuan Zhang
In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild.
Cryptography and Security
no code implementations • 6 Jan 2018 • Di Tang, Zhe Zhou, Yinqian Zhang, Kehuan Zhang
The overall accuracy of our liveness detection system is 98. 8\%, and its robustness was evaluated in different scenarios.
no code implementations • 13 Feb 2018 • Di Tang, XiaoFeng Wang, Kehuan Zhang
To launch black-box attacks against a Deep Neural Network (DNN) based Face Recognition (FR) system, one needs to build \textit{substitute} models to simulate the target model, so the adversarial examples discovered from substitute models could also mislead the target model.
no code implementations • 13 Mar 2018 • Zhe Zhou, Di Tang, Xiao-Feng Wang, Weili Han, Xiangyu Liu, Kehuan Zhang
We propose a kind of brand new attack against face recognition systems, which is realized by illuminating the subject using infrared according to the adversarial examples worked out by our algorithm, thus face recognition systems can be bypassed or misled while simultaneously the infrared perturbations cannot be observed by raw eyes.
Cryptography and Security
1 code implementation • 2 Aug 2019 • Di Tang, Xiao-Feng Wang, Haixu Tang, Kehuan Zhang
A security threat to deep neural networks (DNN) is backdoor contamination, in which an adversary poisons the training data of a target model to inject a Trojan so that images carrying a specific trigger will always be classified into a specific label.
Cryptography and Security
no code implementations • 31 Aug 2019 • Shuaike Dong, Zhou Li, Di Tang, Jiongyi Chen, Menghan Sun, Kehuan Zhang
However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured.
Cryptography and Security
1 code implementation • CVPR 2021 • Zhaoyang Lyu, Minghao Guo, Tong Wu, Guodong Xu, Kehuan Zhang, Dahua Lin
Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks.
no code implementations • 16 Oct 2022 • Hui Liu, Bo Zhao, Kehuan Zhang, Peng Liu
In this paper, we propose an AutoEncoder-based Adversarial Examples (AEAE) detector, that can guard DNN models by detecting adversarial examples with low computation in an unsupervised manner.