Paper Info
Title
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing.
Abstract
Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a normal sample with some carefully crafted human imperceptible perturbations, even highly accurate DNN make wrong decisions. Multiple defense mechanisms have been proposed which aim to hinder the generation of such adversarial samples. However, a recent work show that most of them are ineffective. In this work, we propose an alternative approach to detect adversarial samples at runtime. Our main observation is that adversarial samples are much more sensitive than normal samples if we impose random mutations on the DNN. We thus first propose a measure of 'sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity. We then integrate statistical hypothesis testing and model mutation testing to check whether an input sample is likely to be normal or adversarial at runtime by measuring its sensitivity. We evaluated our approach on the MNIST and CIFAR10 datasets. The results show that our approach detects adversarial samples generated by state-of-the-art attacking methods efficiently and accurately.
Year
DOI
Venue
2018
10.1109/ICSE.2019.00126
Proceedings of the 41st International Conference on Software Engineering
Keywords
Field
DocType
adversarial sample, deep neural network, detection, mutation, sensitivity, testing
MNIST database,Artificial intelligence,Artificial neural network,Statistical hypothesis testing,Machine learning,Mathematics,Deep neural networks,Adversarial system
Journal
Volume
ISSN
ISBN
abs/1812.05793
0270-5257
978-1-7281-0870-4
Citations 
PageRank 
References 
17
0.60
45
Authors
5
Name
Order
Citations
PageRank
wang jingyi17216.19
Guoliang Dong2282.49
Jun Sun31407120.35
xinyu459030.19
Peixin Zhang5444.25