Search Results for author: Kehuan Zhang

Found 8 papers, 2 papers with code

Towards Evaluating and Training Verifiably Robust Neural Networks

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.

Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks

no code implementations31 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

Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection

1 code implementation2 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

Invisible Mask: Practical Attacks on Face Recognition with Infrared

no code implementations13 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

Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints

no code implementations13 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.

Face Recognition

Face Flashing: a Secure Liveness Detection Protocol based on Light Reflections

no code implementations6 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.

Face Verification

Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild

no code implementations5 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

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