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Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B. and Madry, A. (2019) Adversarial Examples Are Not Bugs, They Are Features. 33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, 8-14 December 2019, 125-136.

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• TITLE: Robustness Augmentation of Deep Learning Model Based on Pixel Change

  AUTHORS: Yu Zhang, Hexin Cai

  KEYWORDS: Data Augmentation, Deep Learning, Classification Boundary, Small-Distance Samples

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.14 No.4, April 28, 2021

  ABSTRACT: Deep learning has been widely used in many fields. A large number of images can be quickly recognized by the deep learning models to provide information. How to improve the robustness of deep learning applications has become the focus of research. Unfortunately, the recognition ability of the existing deep learning model has been greatly threatened, many images can cause recognition errors in a well-trained model. Although data augmentation is an effective method, the existence of adversarial examples shows that traditional data augmentation methods have no obvious effect on minor pixel changes. After analyzing the impact of pixel changes on model recognition accuracy, a data augmentation method based on a small number of pixel changes is proposed. Our method can optimize the corresponding classification boundary and improve the recognition robustness of the model. Finally, a simple evaluation method to measure the robustness of model recognition is proposed. Our experiments prove the threat of a small number of pixels and the effectiveness of our data augmentation method. Moreover, the data augmentation method has strong generalization ability and can be applied to image recognition in many different fields.