SESSION 3A-4 NIC: Detecting Adversarial Samples with Neural Network Invariant Checking
Deep Neural Networks (DNN) are vulnerable to adversarial samples that are generated by perturbing correctly classified inputs to cause DNN models to misbehave (e.g., misclassification). This can potentially lead to disastrous consequences especially in security-sensitive applications. Existing defense and detection techniques work well for specific attacks under various assumptions (e.g., the set of possible attacks are known beforehand). However, they are not sufficiently general to protect against a broader range of attacks. In this paper, we analyze the internals of DNN models under various attacks and identify two common exploitation channels: the provenance channel and the activation value distribution channel. We then propose a novel technique to extract DNN invariants and use them to perform runtime adversarial sample detection. Our experimental results of 11 different kinds of attacks on popular datasets including ImageNet and 13 models show that our technique can effectively detect all these attacks (over 90% accuracy) with limited false positives. We also compare it with three state-of-the-art techniques including the Local Intrinsic Dimensionality (LID) based method, denoiser based methods (i.e., MagNet and HGD), and the prediction inconsistency based approach (i.e., feature squeezing). Our experiments show promising results.
PAPER
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SLIDES
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AUTHORS
Shiqing Ma (Purdue University)
Yingqi Liu (Purdue University)
Guanhong Tao (Purdue University)
Wen-Chuan Lee (Purdue University)
Xiangyu Zhang (Purdue University)
Network and Distributed System Security (NDSS) Symposium 2019, 24-27 February 2019, Catamaran Resort Hotel & Spa in San Diego, California.
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The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.
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