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ZHENG Jiamin, ZHANG Yaoyuan, LI Yuanzhang, WU Shangbo, YU Xiao. Towards Evaluating the Robustness of Adversarial Attacks Against Image Scaling Transformation[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.309
Citation: ZHENG Jiamin, ZHANG Yaoyuan, LI Yuanzhang, WU Shangbo, YU Xiao. Towards Evaluating the Robustness of Adversarial Attacks Against Image Scaling Transformation[J]. Chinese Journal of Electronics. doi: 10.1049/cje.2021.00.309

Towards Evaluating the Robustness of Adversarial Attacks Against Image Scaling Transformation

doi: 10.1049/cje.2021.00.309
Funds:  This work was supported by the National Natural Science Foundation of China (61876019, U1936218, and 62072037)
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  • Author Bio:

    Ph.D., Associate Professor in School of E-Business and Logistics at Beijing Technology and Business University. His main research interests include information security, artificial intelligence. (Email: zhengjm@btbu.edu.cn)

    received the BEng degree in Computer Science of Technology from Beijing Institute of Technology, in 2017, where she is currently pursuing the PhD degree. Her research interests include Artificial Intelligence Security. Recently, her research focus has been in the area of computer vision adversarial attack. (Email: yaoyuan@bit.edu.cn)

    received the B.S., M.S., and Ph.D. degrees in software and theory of computer from Beijing Institute of Technology in 2001, 2004, and 2015, respectively. He has been an associate professor with Beijing Institute of Technology. His research interests include mobile computing and information security. (Email: popular@bit.edu.cn)

    graduated from the School of Computer Science and Technology, Beijing Institute of Technology in 2020. He is now a post-graduate in the University of Glasgow majoring in Computing Science. His main research interest lies in the areas of semantic black-box adversarial attacks for both classifiers and object detectors. (Email: wu@bit.edu.cn)

    (corresponding author) Ph.D., Associate professor and master supervisor in Department of Computer Science and Technology, Shandong University of Technology. His current research interests include Artificial Intelligence Security and Embedded System. (Email: yuxiao8907118@163.com)

  • Received Date: 2021-08-26
  • Accepted Date: 2021-11-30
  • Available Online: 2022-01-24
  • The robustness of adversarial examples to image scaling transformation is usually ignored when most existing adversarial attacks are proposed. In contrast, image scaling is often the first step of the model to transfer various sizes of input images into fixed ones. We evaluate the impact of image scaling on the robustness of adversarial examples applied to image classification tasks. We set up an image scaling system to provide a basis for robustness evaluation and conduct experiments in different situations to explore the relationship between image scaling and the robustness of adversarial examples. Experiment results show that various scaling algorithms have a similar impact on the robustness of adversarial examples, but the scaling ratio significantly impacts it.
  • ImageNette is open source at https://github.com/fastai/imagenette
    fast.ai is the first deep learning library to provide a unified interface for all the most commonly used deep learning applications for vision, text, tabular data, time series, and collaborative filtering. Official website: https://www.fast.ai/
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