IB-RAR: Information Bottleneck as Regularizer for Adversarial Robustness

• URL: http://arxiv.org/abs/2302.10896v2
• Date: Wed, 31 May 2023 13:24:13 GMT
• Title: IB-RAR: Information Bottleneck as Regularizer for Adversarial Robustness
• Abstract summary: Experimental results show that our method can be naturally combined with adversarial training and provides consistently better accuracy on new adversarial examples. Our method also provides good robustness for undefended methods, such as training with cross-entropy loss only.
• Score: 6.92902550987764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel method, IB-RAR, which uses Information Bottleneck (IB) to strengthen adversarial robustness for both adversarial training and non-adversarial-trained methods. We first use the IB theory to build regularizers as learning objectives in the loss function. Then, we filter out unnecessary features of intermediate representation according to their mutual information (MI) with labels, as the network trained with IB provides easily distinguishable MI for its features. Experimental results show that our method can be naturally combined with adversarial training and provides consistently better accuracy on new adversarial examples. Our method improves the accuracy by an average of 3.07% against five adversarial attacks for the VGG16 network, trained with three adversarial training benchmarks and the CIFAR-10 dataset. In addition, our method also provides good robustness for undefended methods, such as training with cross-entropy loss only. Finally, in the absence of adversarial training, the VGG16 network trained using our method and the CIFAR-10 dataset reaches an accuracy of 35.86% against PGD examples, while using all layers reaches 25.61% accuracy.

Related papers

• Veriphi: Attack-Guided Neural Network Verification with Dataset-Dependent Training Methods [0.0]
  We present a GPU-accelerated neural network verification system that combines fast adversarial attacks with formal bound certification.<n>We demonstrate that training method effectiveness is fundamentally dataset-dependent.<n>We achieve 5x verification speedup through attack-guided falsification and scale our approach to production-size models.
  arXiv  Detail & Related papers  (2026-06-16T20:02:51Z)
• Wasserstein distributional adversarial training for deep neural networks [4.0984142455934345]
  We propose methods to train against distributional attack threats. We introduce an efficient fine-tuning method which can be deployed on a previously trained model. We test our methods on a range of pre-trained models on RobustBench.
  arXiv  Detail & Related papers  (2025-02-13T14:18:41Z)
• Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection [9.241737058291823]
  Adrial training methods generate independent initial perturbation for adversarial samples from a simple uniform distribution. We propose a simple yet effective training framework called Batch-in-Batch to enhance models. We show that models trained within the BB framework consistently have higher adversarial accuracy across various adversarial settings.
  arXiv  Detail & Related papers  (2024-06-06T13:34:43Z)
• 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)
• Outlier Robust Adversarial Training [57.06824365801612]
  We introduce Outlier Robust Adversarial Training (ORAT) in this work. ORAT is based on a bi-level optimization formulation of adversarial training with a robust rank-based loss function. We show that the learning objective of ORAT satisfies the $mathcalH$-consistency in binary classification, which establishes it as a proper surrogate to adversarial 0/1 loss.
  arXiv  Detail & Related papers  (2023-09-10T21:36:38Z)
• 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)
• CAT:Collaborative Adversarial Training [80.55910008355505]
  We propose a collaborative adversarial training framework to improve the robustness of neural networks. Specifically, we use different adversarial training methods to train robust models and let models interact with their knowledge during the training process. Cat achieves state-of-the-art adversarial robustness without using any additional data on CIFAR-10 under the Auto-Attack benchmark.
  arXiv  Detail & Related papers  (2023-03-27T05:37:43Z)
• Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense [27.545466364906773]
  We present a new algorithm to learn a deep neural network model robust against adversarial attacks. Our model demonstrate significantly improved robustness--up to 20%--compared with adversarial training and Adv-BNN under PGD attacks.
  arXiv  Detail & Related papers  (2022-12-05T03:26:08Z)
• Two Heads are Better than One: Robust Learning Meets Multi-branch Models [14.72099568017039]
  We propose Branch Orthogonality adveRsarial Training (BORT) to obtain state-of-the-art performance with solely the original dataset for adversarial training. We evaluate our approach on CIFAR-10, CIFAR-100, and SVHN against ell_infty norm-bounded perturbations of size epsilon = 8/255, respectively.
  arXiv  Detail & Related papers  (2022-08-17T05:42:59Z)
• Distributed Adversarial Training to Robustify Deep Neural Networks at Scale [100.19539096465101]
  Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective approach, known as adversarial training (AT), has been shown to mitigate robust training. We propose a large-batch adversarial training framework implemented over multiple machines.
  arXiv  Detail & Related papers  (2022-06-13T15:39:43Z)
• Sparsity Winning Twice: Better Robust Generalization from More Efficient Training [94.92954973680914]
  We introduce two alternatives for sparse adversarial training: (i) static sparsity and (ii) dynamic sparsity. We find both methods to yield win-win: substantially shrinking the robust generalization gap and alleviating the robust overfitting. Our approaches can be combined with existing regularizers, establishing new state-of-the-art results in adversarial training.
  arXiv  Detail & Related papers  (2022-02-20T15:52:08Z)
• 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)
• Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning [134.15174177472807]
  We introduce adversarial training into self-supervision, to provide general-purpose robust pre-trained models for the first time. We conduct extensive experiments to demonstrate that the proposed framework achieves large performance margins.
  arXiv  Detail & Related papers  (2020-03-28T18:28:33Z)

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.