48 citations · 51 across the 2 of their papers we have counts for
8 papers
Nowhere to Hide: A Lightweight Unsupervised Detector against Adversarial Examples
Hui Liu, Bo Zhao, Kehuan Zhang +1
Although deep neural networks (DNNs) have shown impressive performance on many perceptual tasks, they are vulnerable to adversarial examples that are generated by adding slight but…
Towards Evaluating and Training Verifiably Robust Neural Networks
Zhaoyang Lyu, Minghao Guo, Tong Wu +3
Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP train…
Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
Shuaike Dong, Zhou Li, Di Tang +3
The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing t…
Demon in the Variant: Statistical Analysis of DNNs for Robust Backdoor Contamination Detection
Di Tang, XiaoFeng Wang, Haixu Tang +1
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 carry…
Invisible Mask: Practical Attacks on Face Recognition with Infrared
Zhe Zhou, Di Tang, Xiaofeng Wang +3
Accurate face recognition techniques make a series of critical applications possible: policemen could employ it to retrieve criminals' faces from surveillance video streams; cross…
Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints
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 t…