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On the Adversarial Transferability of ConvMixer Models

arXiv 2209.08724 ↗First posted 19 Sep 2022cs.LGcited by 0

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Abstract (arXiv)

Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs). In addition, AEs have adversarial transferability, which means AEs generated for a source model can fool another black-box model (target model) with a non-trivial probability. In this paper, we investigate the property of adversarial transferability between models including ConvMixer, which is an isotropic network, for the first time. To objectively verify the property of transferability, the robustness of models is evaluated by using a benchmark attack method called AutoAttack. In an image classification experiment, ConvMixer is confirmed to be weak to adversarial transferability.

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References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  5. 2013Intriguing properties of neural networkscited by 5,155
  6. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,451
  7. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 685
  8. 2017One pixel attack for fooling deep neural networkscited by 582
  9. 2016Delving into Transferable Adversarial Examples and Black-box Attackscited by 536
  10. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 446
  11. 2019Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attackcited by 334
  12. 2022Patches Are All You Need?cited by 201
10 more
  1. 2021On the Robustness of Vision Transformers to Adversarial Examplescited by 167
  2. 2019NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networkscited by 94
  3. 2021On Improving Adversarial Transferability of Vision Transformerscited by 70
  4. 2018Encryption-then-Compression Systems using Grayscale-based Image Encryption for JPEG Imagescited by 34
  5. 2018Learnable Image Encryptioncited by 33
  6. 2020Block-wise Image Transformation with Secret Key for Adversarially Robust Defensecited by 30
  7. 2022An Overview of Compressible and Learnable Image Transformation with Secret Key and Its Applicationscited by 29
  8. 2020Block-wise Scrambled Image Recognition Using Adaptation Networkcited by 27
  9. 2020A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networkscited by 27
  10. 2022On the Transferability of Adversarial Examples between Encrypted Modelscited by 4

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