Improving diversity and quality of adversarial examples in adversarial transformation network

This paper proposes PatternAttack to mitigate two major issues of Adversarial Transformation Network (ATN) including the low diversity and the low quality of adversarial examples. In order to deal with the first issue, this research proposes a stacked convolutional autoencoder based on patterns to g...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 27; no. 7; pp. 3689 - 3706
Main Authors: Nguyen, Duc-Anh, Minh, Kha Do, Le, Khoi Nguyen, Nguyen, Le-Minh, Hung, Pham Ngoc
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023
Subjects:
Artificial Intelligence
Computational Intelligence
Control
Data Analytics and Machine Learning
Engineering
Mathematical Logic and Foundations
Mechatronics
Robotics
Adversarial example
Targeted attack
Robustness
Stacked convolutional autoencoder
ISSN:1432-7643, 1433-7479
Online Access:Get full text
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Summary:This paper proposes PatternAttack to mitigate two major issues of Adversarial Transformation Network (ATN) including the low diversity and the low quality of adversarial examples. In order to deal with the first issue, this research proposes a stacked convolutional autoencoder based on patterns to generalize ATN. This proposed autoencoder could support different patterns such as all-pixel pattern , object boundary pattern , and class model map pattern . In order to deal with the second issue, this paper presents an algorithm to improve the quality of adversarial examples in terms of L 0 -norm and L 2 -norm. This algorithm employs adversarial pixel ranking heuristics such as JSMA and COI to prioritize adversarial pixels. To demonstrate the advantages of the proposed method, comprehensive experiments have been conducted on the MNIST dataset and the CIFAR-10 dataset. For the first issue, the proposed autoencoder generates diverse adversarial examples. For the second issue, the proposed algorithm significantly improves the quality of adversarial examples. In terms of L 0 -norm, the proposed algorithm decreases from hundreds of adversarial pixels to one adversarial pixel. In terms of L 2 -norm, the proposed algorithm reduces the average distance considerably. These results show that the proposed method can generate high-quality and diverse adversarial examples in practice.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-022-07655-y