mlsanityData to 7 Oct 2026

Robust and Efficient Interference Neural Networks for Defending Against Adversarial Attacks in ImageNet

arXiv 2310.05947 ↗First posted 3 Sep 2023cs.CV cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

The existence of adversarial images has seriously affected the task of image recognition and practical application of deep learning, it is also a key scientific problem that deep learning urgently needs to solve. By far the most effective approach is to train the neural network with a large number of adversarial examples. However, this adversarial training method requires a huge amount of computing resources when applied to ImageNet, and has not yet achieved satisfactory results for high-intensity adversarial attacks. In this paper, we construct an interference neural network by applying additional background images and corresponding labels, and use pre-trained ResNet-152 to efficiently complete the training. Compared with the state-of-the-art results under the PGD attack, it has a better defense effect with much smaller computing resources. This work provides new ideas for academic research and practical applications of effective defense against adversarial attacks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 27 references that are in the map. First posted on arXiv 3 Sep 2023.

Builds on

20 of 27 references.

References · 20

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  4. 2016Adversarial examples in the physical worldcited by 1,762
  5. 2017Ensemble Adversarial Training: Attacks and Defensescited by 833
  6. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
  7. 2020Fast is better than free: Revisiting adversarial trainingcited by 723
  8. 2019Adversarial Training for Free!cited by 638
  9. 2017One pixel attack for fooling deep neural networkscited by 579
  10. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 511
  11. 2018Feature Denoising for Improving Adversarial Robustnesscited by 312
  12. 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 276
8 more
  1. 2018Adversarial Logit Pairingcited by 224
  2. 2020Understanding and Improving Fast Adversarial Trainingcited by 213
  3. 2019Adversarial Robustness through Local Linearizationcited by 146
  4. 2018Towards the first adversarially robust neural network model on MNISTcited by 104
  5. 2020Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalizationcited by 73
  6. 2019Instance adaptive adversarial training: Improved accuracy tradeoffs in neural netscited by 55
  7. 2023Towards quantum enhanced adversarial robustness in machine learningcited by 26
  8. 2023Unlocking Deterministic Robustness Certification on ImageNetcited by 13

Built on by

Not cited yet.