Yijun Yang (The Chinese University of Hong Kong), Ruiyuan Gao (The Chinese University of Hong Kong), Yu Li (The Chinese University of Hong Kong), Qiuxia Lai (Communication University of China), Qiang Xu (The Chinese University of Hong Kong)

Adversarial examples (AEs) pose severe threats to the applications of deep neural networks (DNNs) to safety-critical domains (e.g., autonomous driving and healthcare analytics). While there has been a vast body of research to defend against AEs, to the best of our knowledge, they all suffer from some weaknesses, e.g., defending against only a subset of AEs or causing a relatively high accuracy loss for legitimate inputs. Moreover, most of the existing solutions cannot defend against adaptive attacks, wherein attackers are knowledgeable about the defense mechanisms and craft AEs accordingly.

In this paper, we propose a novel AE detection framework based on the very nature of AEs, i.e., their semantic information is inconsistent with the discriminative features extracted by the target DNN model. To be specific, the proposed solution, namely ContraNet, models such contradiction by first taking both the input and the inference result to a generator to obtain a synthetic output and then comparing it against the original input. For legitimate inputs that are correctly inferred, the synthetic output tries to reconstruct the input. On the contrary, for AEs, instead of reconstructing the input, the synthetic output would be created to conform to the wrong label whenever possible. Consequently, by measuring the distance between the input and the synthetic output with metric learning, we can differentiate AEs from legitimate inputs. We perform comprehensive evaluations under various types of AE attack scenarios, and experimental results show that ContraNet outperforms existing solutions by a large margin, especially for adaptive attacks. Moreover, further analysis shows that successful AEs that can bypass ContraNet tend to have much-weakened adversarial semantics. We have also shown that ContraNet can be easily combined with adversarial training techniques to achieve more outstanding AE defense capabilities.

View More Papers

Semantic-Informed Driver Fuzzing Without Both the Hardware Devices and...

Wenjia Zhao (Xi'an Jiaotong University and University of Minnesota), Kangjie Lu (University of Minnesota), Qiushi Wu (University of Minnesota), Yong Qi (Xi'an Jiaotong University)

Read More

Drivers and Passengers Maybe the Weakest Link in the...

Aiping Xiong (Pennsylvania State University), Zekun Cai (Pennsylvania State University) and Tianhao Wang (University of Virginia)

Read More

Problematic Content in Online Ads

Franzisca Roesner (University of Washington)

Read More

P4DDPI: Securing P4-Programmable Data Plane Networks via DNS Deep...

Ali AlSabeh (University of South Carolina), Elie Kfoury (University of South Carolina), Jorge Crichigno (University of South Carolina) and Elias Bou-Harb (University of Texas at San Antonio)

Read More