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Versatile Defense Against Adversarial Attacks on Image Recognition

arXiv 2403.08170 ↗First posted 13 Mar 2024cs.CV eess.IVcited by 0

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Abstract (arXiv)

Adversarial attacks present a significant security risk to image recognition tasks. Defending against these attacks in a real-life setting can be compared to the way antivirus software works, with a key consideration being how well the defense can adapt to new and evolving attacks. Another important factor is the resources involved in terms of time and cost for training defense models and updating the model database. Training many models that are specific to each type of attack can be time-consuming and expensive. Ideally, we should be able to train one single model that can handle a wide range of attacks. It appears that a defense method based on image-to-image translation may be capable of this. The proposed versatile defense approach in this paper only requires training one model to effectively resist various unknown adversarial attacks. The trained model has successfully improved the classification accuracy from nearly zero to an average of 86%, performing better than other defense methods proposed in prior studies. When facing the PGD attack and the MI-FGSM attack, versatile defense model even outperforms the attack-specific models trained based on these two attacks. The robustness check also shows that our versatile defense model performs stably regardless with the attack strength.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 24 references that are in the map. First posted on arXiv 13 Mar 2024.

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References · 22

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  5. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  6. 2022Generative Adversarial Networkscited by 5,603
  7. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  8. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  9. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  10. 2014Conditional Generative Adversarial Netscited by 2,152
  11. 2016Adversarial examples in the physical worldcited by 1,762
  12. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,442
10 more
  1. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  2. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  3. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  4. 2017Mitigating Adversarial Effects Through Randomizationcited by 382
  5. 2018Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Modelscited by 362
  6. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 234
  7. 2018Adversarial Robustness Toolbox v1.0.0cited by 214
  8. 2016Technical Report on the CleverHans v2.1.0 Adversarial Examples Librarycited by 123
  9. 2018Deflecting Adversarial Attacks with Pixel Deflectioncited by 71
  10. 2019Image Super-Resolution as a Defense Against Adversarial Attackscited by 64

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