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Revisiting Min-Max Optimization Problem in Adversarial Training

arXiv 2408.11218 ↗First posted 20 Aug 2024cs.CV cs.LGcited by 0

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Abstract (arXiv)

The rise of computer vision applications in the real world puts the security of the deep neural networks at risk. Recent works demonstrate that convolutional neural networks are susceptible to adversarial examples - where the input images look similar to the natural images but are classified incorrectly by the model. To provide a rebuttal to this problem, we propose a new method to build robust deep neural networks against adversarial attacks by reformulating the saddle point optimization problem in citemadry2017towards. Our proposed method offers significant resistance and a concrete security guarantee against multiple adversaries. The goal of this paper is to act as a stepping stone for a new variation of deep learning models which would lead towards fully robust deep learning models.

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References · 18

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  4. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  5. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,442
  6. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  7. 2018Robustness May Be at Odds with Accuracycited by 873
  8. 2020Fast is better than free: Revisiting adversarial trainingcited by 723
  9. 2019Unlabeled Data Improves Adversarial Robustnesscited by 415
  10. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  11. 2019Using Pre-Training Can Improve Model Robustness and Uncertaintycited by 373
  12. 2019FreeLB: Enhanced Adversarial Training for Natural Language Understandingcited by 261
6 more
  1. 2018Towards Fast Computation of Certified Robustness for ReLU Networkscited by 254
  2. 2020Understanding and Improving Fast Adversarial Trainingcited by 213
  3. 2019Are Labels Required for Improving Adversarial Robustness?cited by 162
  4. 2015Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimizationcited by 32
  5. 2024AdaFed: Fair Federated Learning via Adaptive Common Descent Directioncited by 12
  6. 2024Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopescited by 3

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