Feature Importance-aware Transferable Adversarial Attacks

• URL: http://arxiv.org/abs/2107.14185v1
• Date: Thu, 29 Jul 2021 17:13:29 GMT
• Title: Feature Importance-aware Transferable Adversarial Attacks
• Authors: Zhibo Wang, Hengchang Guo, Zhifei Zhang, Wenxin Liu, Zhan Qin, Kui Ren
• Abstract summary: Existing transferable attacks tend to craft adversarial examples by indiscriminately distorting features. We argue that such brute-force degradation would introduce model-specific local optimum into adversarial examples. By contrast, we propose the Feature Importance-aware Attack (FIA), which disrupts important object-aware features.
• Score: 46.12026564065764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transferability of adversarial examples is of central importance for attacking an unknown model, which facilitates adversarial attacks in more practical scenarios, e.g., blackbox attacks. Existing transferable attacks tend to craft adversarial examples by indiscriminately distorting features to degrade prediction accuracy in a source model without aware of intrinsic features of objects in the images. We argue that such brute-force degradation would introduce model-specific local optimum into adversarial examples, thus limiting the transferability. By contrast, we propose the Feature Importance-aware Attack (FIA), which disrupts important object-aware features that dominate model decisions consistently. More specifically, we obtain feature importance by introducing the aggregate gradient, which averages the gradients with respect to feature maps of the source model, computed on a batch of random transforms of the original clean image. The gradients will be highly correlated to objects of interest, and such correlation presents invariance across different models. Besides, the random transforms will preserve intrinsic features of objects and suppress model-specific information. Finally, the feature importance guides to search for adversarial examples towards disrupting critical features, achieving stronger transferability. Extensive experimental evaluation demonstrates the effectiveness and superior performance of the proposed FIA, i.e., improving the success rate by 8.4% against normally trained models and 11.7% against defense models as compared to the state-of-the-art transferable attacks. Code is available at: https://github.com/hcguoO0/FIA

Related papers

• Boosting the Targeted Transferability of Adversarial Examples via Salient Region & Weighted Feature Drop [2.176586063731861]
  A prevalent approach for adversarial attacks relies on the transferability of adversarial examples. A novel framework based on Salient region & Weighted Feature Drop (SWFD) designed to enhance the targeted transferability of adversarial examples.
  arXiv  Detail & Related papers  (2024-11-11T08:23:37Z)
• SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation [56.622250514119294]
  In contrast to white-box adversarial attacks, transfer attacks are more reflective of real-world scenarios. We propose a self-augment-based transfer attack method, termed SA-Attack.
  arXiv  Detail & Related papers  (2023-12-08T09:08:50Z)
• An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability [26.39964737311377]
  We propose an adaptive ensemble attack, dubbed AdaEA, to adaptively control the fusion of the outputs from each model. We achieve considerable improvement over the existing ensemble attacks on various datasets.
  arXiv  Detail & Related papers  (2023-08-05T15:12:36Z)
• Rethinking Model Ensemble in Transfer-based Adversarial Attacks [46.82830479910875]
  An effective strategy to improve the transferability is attacking an ensemble of models. Previous works simply average the outputs of different models. We propose a Common Weakness Attack (CWA) to generate more transferable adversarial examples.
  arXiv  Detail & Related papers  (2023-03-16T06:37:16Z)
• 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)
• Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition [111.1952945740271]
  Adversarial Attributes (Adv-Attribute) is designed to generate inconspicuous and transferable attacks on face recognition. Experiments on the FFHQ and CelebA-HQ datasets show that the proposed Adv-Attribute method achieves the state-of-the-art attacking success rates.
  arXiv  Detail & Related papers  (2022-10-13T09:56:36Z)
• Harnessing Perceptual Adversarial Patches for Crowd Counting [92.79051296850405]
  Crowd counting is vulnerable to adversarial examples in the physical world. This paper proposes the Perceptual Adrial Patch (PAP) generation framework to learn the shared perceptual features between models.
  arXiv  Detail & Related papers  (2021-09-16T13:51:39Z)
• 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)
• TREND: Transferability based Robust ENsemble Design [6.663641564969944]
  We study the effect of network architecture, input, weight and activation quantization on transferability of adversarial samples. We show that transferability is significantly hampered by input quantization between source and target. We propose a new state-of-the-art ensemble attack to combat this.
  arXiv  Detail & Related papers  (2020-08-04T13:38:14Z)
• Luring of transferable adversarial perturbations in the black-box paradigm [0.0]
  We present a new approach to improve the robustness of a model against black-box transfer attacks. A removable additional neural network is included in the target model, and is designed to induce the textitluring effect. Our deception-based method only needs to have access to the predictions of the target model and does not require a labeled data set.
  arXiv  Detail & Related papers  (2020-04-10T06:48:36Z)

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.