Adaptive Feature Alignment for Adversarial Training

• URL: http://arxiv.org/abs/2105.15157v1
• Date: Mon, 31 May 2021 17:01:05 GMT
• Title: Adaptive Feature Alignment for Adversarial Training
• Authors: Tao Wang and Ruixin Zhang and Xingyu Chen and Kai Zhao and Xiaolin Huang and Yuge Huang and Shaoxin Li and Jilin Li and Feiyue Huang
• Abstract summary: CNNs are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. We propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths. Our method is trained to automatically align features of arbitrary attacking strength.
• Score: 56.17654691470554
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent studies reveal that Convolutional Neural Networks (CNNs) are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. Many adversarial defense methods improve robustness at the cost of accuracy, raising the contradiction between standard and adversarial accuracies. In this paper, we observe an interesting phenomenon that feature statistics change monotonically and smoothly w.r.t the rising of attacking strength. Based on this observation, we propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths. Our method is trained to automatically align features of arbitrary attacking strength. This is done by predicting a fusing weight in a dual-BN architecture. Unlike previous works that need to either retrain the model or manually tune a hyper-parameters for different attacking strengths, our method can deal with arbitrary attacking strengths with a single model without introducing any hyper-parameter. Importantly, our method improves the model robustness against adversarial samples without incurring much loss in standard accuracy. Experiments on CIFAR-10, SVHN, and tiny-ImageNet datasets demonstrate that our method outperforms the state-of-the-art under a wide range of attacking strengths.

Related papers

• Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks [62.036798488144306]
  Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. We propose an attack-agnostic defense method named Meta Invariance Defense (MID) We show that MID simultaneously achieves robustness to the imperceptible adversarial perturbations in high-level image classification and attack-suppression in low-level robust image regeneration.
  arXiv  Detail & Related papers  (2024-04-04T10:10:38Z)
• Adversarial Attacks Neutralization via Data Set Randomization [3.655021726150369]
  Adversarial attacks on deep learning models pose a serious threat to their reliability and security. We propose a new defense mechanism that is rooted on hyperspace projection. We show that our solution increases the robustness of deep learning models against adversarial attacks.
  arXiv  Detail & Related papers  (2023-06-21T10:17:55Z)
• Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning [80.21709045433096]
  A standard method in adversarial robustness assumes a framework to defend against samples crafted by minimally perturbing a sample. We use metric learning to frame adversarial regularization as an optimal transport problem. Our preliminary results indicate that regularizing over invariant perturbations in our framework improves both invariant and sensitivity defense.
  arXiv  Detail & Related papers  (2022-11-04T13:54:02Z)
• Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses [82.3052187788609]
  Adversarial training (AT) is considered to be one of the most reliable defenses against adversarial attacks. Recent works show generalization improvement with adversarial samples under novel threat models. We propose a novel threat model called Joint Space Threat Model (JSTM) Under JSTM, we develop novel adversarial attacks and defenses.
  arXiv  Detail & Related papers  (2021-12-12T21:08:14Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Self-Progressing Robust Training [146.8337017922058]
  Current robust training methods such as adversarial training explicitly uses an "attack" to generate adversarial examples. We propose a new framework called SPROUT, self-progressing robust training. Our results shed new light on scalable, effective and attack-independent robust training methods.
  arXiv  Detail & Related papers  (2020-12-22T00:45:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.