Adversarial Robust Memory-Based Continual Learner

• URL: http://arxiv.org/abs/2311.17608v1
• Date: Wed, 29 Nov 2023 13:05:20 GMT
• Title: Adversarial Robust Memory-Based Continual Learner
• Authors: Xiaoyue Mi, Fan Tang, Zonghan Yang, Danding Wang, Juan Cao, Peng Li, Yang Liu
• Abstract summary: In this study, we put forward a novel adversarial robust memory-based continual learner. We devise a gradient-based data selection mechanism to overcome the gradient obfuscation caused by limited stored data. Experiments on Split-CIFAR10/100 and Split-Tiny-ImageNet demonstrate the effectiveness of our approach, achieving up to 8.13% higher accuracy for adversarial data.
• Score: 32.70573627856543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the remarkable advances that have been made in continual learning, the adversarial vulnerability of such methods has not been fully discussed. We delve into the adversarial robustness of memory-based continual learning algorithms and observe limited robustness improvement by directly applying adversarial training techniques. Preliminary studies reveal the twin challenges for building adversarial robust continual learners: accelerated forgetting in continual learning and gradient obfuscation in adversarial robustness. In this study, we put forward a novel adversarial robust memory-based continual learner that adjusts data logits to mitigate the forgetting of pasts caused by adversarial samples. Furthermore, we devise a gradient-based data selection mechanism to overcome the gradient obfuscation caused by limited stored data. The proposed approach can widely integrate with existing memory-based continual learning as well as adversarial training algorithms in a plug-and-play way. Extensive experiments on Split-CIFAR10/100 and Split-Tiny-ImageNet demonstrate the effectiveness of our approach, achieving up to 8.13% higher accuracy for adversarial data.

Related papers

• Maintaining Adversarial Robustness in Continuous Learning [10.746120682014437]
  Adversarial robustness enhanced by defense algorithms is easily erased as the neural network's weights update to learn new tasks. We propose a novel gradient projection technique that effectively stabilizes sample gradients from previous data. This technique can maintaining robustness by collaborating with a class of defense algorithms through sample gradient smoothing.
  arXiv  Detail & Related papers  (2024-02-17T05:14:47Z)
• Class Incremental Learning for Adversarial Robustness [17.06592851567578]
  Adrial training integrates adversarial examples during model training to enhance robustness. We observe that combining incremental learning with naive adversarial training easily leads to a loss of robustness. We propose the Flatness Preserving Distillation (FPD) loss that leverages the output difference between adversarial and clean examples.
  arXiv  Detail & Related papers  (2023-12-06T04:38:02Z)
• Enhancing Adversarial Training via Reweighting Optimization Trajectory [72.75558017802788]
  A number of approaches have been proposed to address drawbacks such as extra regularization, adversarial weights, and training with more data. We propose a new method named textbfWeighted Optimization Trajectories (WOT) that leverages the optimization trajectories of adversarial training in time. Our results show that WOT integrates seamlessly with the existing adversarial training methods and consistently overcomes the robust overfitting issue.
  arXiv  Detail & Related papers  (2023-06-25T15:53:31Z)
• Adversarial Training Should Be Cast as a Non-Zero-Sum Game [121.95628660889628]
  Two-player zero-sum paradigm of adversarial training has not engendered sufficient levels of robustness. We show that the commonly used surrogate-based relaxation used in adversarial training algorithms voids all guarantees on robustness. A novel non-zero-sum bilevel formulation of adversarial training yields a framework that matches and in some cases outperforms state-of-the-art attacks.
  arXiv  Detail & Related papers  (2023-06-19T16:00:48Z)
• Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training [2.538209532048867]
  We introduce a novel neural network training framework that increases model's adversarial robustness to adversarial attacks. We propose to improve model robustness to adversarial attacks by learning feature representations consistent under both data augmentations and adversarial perturbations. We validate our method on the CIFAR-10 dataset on which it outperforms both robust accuracy and clean accuracy over alternative supervised and self-supervised adversarial learning methods.
  arXiv  Detail & Related papers  (2022-03-16T21:41:27Z)
• Learning to Learn Transferable Attack [77.67399621530052]
  Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. We propose a Learning to Learn Transferable Attack (LLTA) method, which makes the adversarial perturbations more generalized via learning from both data and model augmentation. Empirical results on the widely-used dataset demonstrate the effectiveness of our attack method with a 12.85% higher success rate of transfer attack compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-10T07:24:21Z)
• Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning [114.9857000195174]
  A major challenge to widespread industrial adoption of deep reinforcement learning is the potential vulnerability to privacy breaches. We propose an adversarial attack framework tailored for testing the vulnerability of deep reinforcement learning algorithms to membership inference attacks.
  arXiv  Detail & Related papers  (2021-09-08T23:44:57Z)
• Adversarial Self-Supervised Contrastive Learning [62.17538130778111]
  Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions. We propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples. We present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data.
  arXiv  Detail & Related papers  (2020-06-13T08:24:33Z)
• Towards Certified Robustness of Distance Metric Learning [53.96113074344632]
  We advocate imposing an adversarial margin in the input space so as to improve the generalization and robustness of metric learning algorithms. We show that the enlarged margin is beneficial to the generalization ability by using the theoretical technique of algorithmic robustness.
  arXiv  Detail & Related papers  (2020-06-10T16:51:53Z)

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.