Adversarial examples (AEs) pose severe threats to the applications of deep neural networks (DNNs) to safety-critical domains, e.g., autonomous driving. While there has been a vast body of AE defense solutions, 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 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 AE attack scenarios, and experimental results show that ContraNet outperforms existing solutions by a large margin, especially under adaptive attacks. Moreover, our 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 further improved AE defense capabilities.

Machine learning with deep neural networks (DNNs) has become one of the foundation techniques in many safety-critical systems, such as autonomous vehicles and medical diagnosis systems. DNN-based systems, however, are known to be vulnerable to adversarial examples (AEs) that are maliciously perturbed variants of legitimate inputs. While there has been a vast body of research to defend against AE attacks in the literature, the performances of existing defense techniques are still far from satisfactory, especially for adaptive attacks, wherein attackers are knowledgeable about the defense mechanisms and craft AEs accordingly. In this work, we propose a multilayer defense-in-depth framework for AE detection, namely MixDefense. For the first layer, we focus on those AEs with large perturbations. We propose to leverage the `noise' features extracted from the inputs to discover the statistical difference between natural images and tampered ones for AE detection. For AEs with small perturbations, the inference result of such inputs would largely deviate from their semantic information. Consequently, we propose a novel learning-based solution to model such contradictions for AE detection. Both layers are resilient to adaptive attacks because there do not exist gradient propagation paths for AE generation. Experimental results with various AE attack methods on image classification datasets show that the proposed MixDefense solution outperforms the existing AE detection techniques by a considerable margin.