Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations

• URL: http://arxiv.org/abs/2108.09713v1
• Date: Sun, 22 Aug 2021 13:15:24 GMT
• Title: Robustness-via-Synthesis: Robust Training with Generative Adversarial Perturbations
• Authors: Inci M. Baytas and Debayan Deb
• Abstract summary: Adversarial training with first-order attacks has been one of the most effective defenses against adversarial perturbations to this day. This study presents a robust training algorithm where the adversarial perturbations are automatically synthesized from a random vector using a generator network. Experimental results show that the proposed approach attains comparable robustness with various gradient-based and generative robust training techniques.
• Score: 10.140147080535224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Upon the discovery of adversarial attacks, robust models have become obligatory for deep learning-based systems. Adversarial training with first-order attacks has been one of the most effective defenses against adversarial perturbations to this day. The majority of the adversarial training approaches focus on iteratively perturbing each pixel with the gradient of the loss function with respect to the input image. However, the adversarial training with gradient-based attacks lacks diversity and does not generalize well to natural images and various attacks. This study presents a robust training algorithm where the adversarial perturbations are automatically synthesized from a random vector using a generator network. The classifier is trained with cross-entropy loss regularized with the optimal transport distance between the representations of the natural and synthesized adversarial samples. Unlike prevailing generative defenses, the proposed one-step attack generation framework synthesizes diverse perturbations without utilizing gradient of the classifier's loss. Experimental results show that the proposed approach attains comparable robustness with various gradient-based and generative robust training techniques on CIFAR10, CIFAR100, and SVHN datasets. In addition, compared to the baselines, the proposed robust training framework generalizes well to the natural samples. Code and trained models will be made publicly available.

Related papers

• How Robust Are Energy-Based Models Trained With Equilibrium Propagation? [4.374837991804085]
  Adrial training is the current state-of-the-art defense against adversarial attacks. It lowers the model's accuracy on clean inputs, is computationally expensive, and offers less robustness to natural noise. In contrast, energy-based models (EBMs) incorporate feedback connections from each layer to the previous layer, yielding a recurrent, deep-attractor architecture.
  arXiv  Detail & Related papers  (2024-01-21T16:55:40Z)
• Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models [17.283914361697818]
  Deep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO) This paper presents a novel Universal Adversarial Defense approach in Remote Sensing Imagery (UAD-RS)
  arXiv  Detail & Related papers  (2023-07-31T17:21:23Z)
• 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)
• Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness [1.2289361708127877]
  CARSO is able to defend itself against adaptive end-to-end white-box attacks devised for defences. Our method improves by a significant margin the state-of-the-art for CIFAR-10, CIFAR-100, and TinyImageNet-200.
  arXiv  Detail & Related papers  (2023-05-25T09:04:31Z)
• Resisting Adversarial Attacks in Deep Neural Networks using Diverse Decision Boundaries [12.312877365123267]
  Deep learning systems are vulnerable to crafted adversarial examples, which may be imperceptible to the human eye, but can lead the model to misclassify. We develop a new ensemble-based solution that constructs defender models with diverse decision boundaries with respect to the original model. We present extensive experimentations using standard image classification datasets, namely MNIST, CIFAR-10 and CIFAR-100 against state-of-the-art adversarial attacks.
  arXiv  Detail & Related papers  (2022-08-18T08:19:26Z)
• 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)
• Self-Ensemble Adversarial Training for Improved Robustness [14.244311026737666]
  Adversarial training is the strongest strategy against various adversarial attacks among all sorts of defense methods. Recent works mainly focus on developing new loss functions or regularizers, attempting to find the unique optimal point in the weight space. We devise a simple but powerful emphSelf-Ensemble Adversarial Training (SEAT) method for yielding a robust classifier by averaging weights of history models.
  arXiv  Detail & Related papers  (2022-03-18T01:12:18Z)
• Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations [70.05004034081377]
  We first propose a novel method for generating composite adversarial examples. Our method can find the optimal attack composition by utilizing component-wise projected gradient descent. We then propose generalized adversarial training (GAT) to extend model robustness from $ell_p$-ball to composite semantic perturbations.
  arXiv  Detail & Related papers  (2022-02-09T02:41:56Z)
• Adaptive Feature Alignment for Adversarial Training [56.17654691470554]
  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.
  arXiv  Detail & Related papers  (2021-05-31T17:01:05Z)
• Guided Interpolation for Adversarial Training [73.91493448651306]
  As training progresses, the training data becomes less and less attackable, undermining the robustness enhancement. We propose the guided framework (GIF), which employs the previous epoch's meta information to guide the data's adversarial variants. Compared with the vanilla mixup, the GIF can provide a higher ratio of attackable data, which is beneficial to the robustness enhancement.
  arXiv  Detail & Related papers  (2021-02-15T03:55:08Z)
• 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.