Fixed Inter-Neuron Covariability Induces Adversarial Robustness

• URL: http://arxiv.org/abs/2308.03956v1
• Date: Mon, 7 Aug 2023 23:46:14 GMT
• Title: Fixed Inter-Neuron Covariability Induces Adversarial Robustness
• Authors: Muhammad Ahmed Shah and Bhiksha Raj
• Abstract summary: The vulnerability to adversarial perturbations is a major flaw of Deep Neural Networks (DNNs) We have developed the Self-Consistent Activation layer, which comprises of neurons whose activations are consistent with each other, as they conform to a fixed, but learned, covariability pattern. The models with a SCA layer achieved high accuracy, and exhibited significantly greater robustness than multi-layer perceptron models to state-of-the-art Auto-PGD adversarial attacks textitwithout being trained on adversarially perturbed data.
• Score: 26.878913741674058
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The vulnerability to adversarial perturbations is a major flaw of Deep Neural Networks (DNNs) that raises question about their reliability when in real-world scenarios. On the other hand, human perception, which DNNs are supposed to emulate, is highly robust to such perturbations, indicating that there may be certain features of the human perception that make it robust but are not represented in the current class of DNNs. One such feature is that the activity of biological neurons is correlated and the structure of this correlation tends to be rather rigid over long spans of times, even if it hampers performance and learning. We hypothesize that integrating such constraints on the activations of a DNN would improve its adversarial robustness, and, to test this hypothesis, we have developed the Self-Consistent Activation (SCA) layer, which comprises of neurons whose activations are consistent with each other, as they conform to a fixed, but learned, covariability pattern. When evaluated on image and sound recognition tasks, the models with a SCA layer achieved high accuracy, and exhibited significantly greater robustness than multi-layer perceptron models to state-of-the-art Auto-PGD adversarial attacks \textit{without being trained on adversarially perturbed data

Related papers

• Explainable AI Security: Exploring Robustness of Graph Neural Networks to Adversarial Attacks [14.89001880258583]
  Graph neural networks (GNNs) have achieved tremendous success, but recent studies have shown that GNNs are vulnerable to adversarial attacks. We investigate the adversarial robustness of GNNs by considering graph data patterns, model-specific factors, and the transferability of adversarial examples. This work illuminates the vulnerabilities of GNNs and opens many promising avenues for designing robust GNNs.
  arXiv  Detail & Related papers  (2024-06-20T01:24:18Z)
• Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency [61.394997313144394]
  Catastrophic overfitting (CO) presents a significant challenge in single-step adversarial training (AT) We show that during CO, the former layers are more susceptible, experiencing earlier and greater distortion, while the latter layers show relative insensitivity. Our proposed method, Layer-Aware Adversarial Weight Perturbation (LAP), can effectively prevent CO and further enhance robustness.
  arXiv  Detail & Related papers  (2024-05-25T14:56:30Z)
• Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception [10.883174135300418]
  Adversarial attacks have long been considered the "Achilles' heel" of deep learning. Here, we investigate how the robustness of DNNs to adversarial attacks has evolved as their accuracy on ImageNet has continued to improve.
  arXiv  Detail & Related papers  (2023-06-05T20:26:17Z)
• Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classification [6.594522185216161]
  We introduce sensible adversarial learning and demonstrate the synergistic effect between pursuits of standard natural accuracy and robustness. Specifically, we define a sensible adversary which is useful for learning a robust model while keeping high natural accuracy. We propose a novel and efficient algorithm that trains a robust model using implicit loss truncation.
  arXiv  Detail & Related papers  (2022-05-20T22:57:44Z)
• Neural Architecture Dilation for Adversarial Robustness [56.18555072877193]
  A shortcoming of convolutional neural networks is that they are vulnerable to adversarial attacks. This paper aims to improve the adversarial robustness of the backbone CNNs that have a satisfactory accuracy. Under a minimal computational overhead, a dilation architecture is expected to be friendly with the standard performance of the backbone CNN.
  arXiv  Detail & Related papers  (2021-08-16T03:58:00Z)
• Residual Error: a New Performance Measure for Adversarial Robustness [85.0371352689919]
  A major challenge that limits the wide-spread adoption of deep learning has been their fragility to adversarial attacks. This study presents the concept of residual error, a new performance measure for assessing the adversarial robustness of a deep neural network. Experimental results using the case of image classification demonstrate the effectiveness and efficacy of the proposed residual error metric.
  arXiv  Detail & Related papers  (2021-06-18T16:34:23Z)
• On the benefits of robust models in modulation recognition [53.391095789289736]
  Deep Neural Networks (DNNs) using convolutional layers are state-of-the-art in many tasks in communications. In other domains, like image classification, DNNs have been shown to be vulnerable to adversarial perturbations. We propose a novel framework to test the robustness of current state-of-the-art models.
  arXiv  Detail & Related papers  (2021-03-27T19:58:06Z)
• Non-Singular Adversarial Robustness of Neural Networks [58.731070632586594]
  Adrial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. We formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
  arXiv  Detail & Related papers  (2021-02-23T20:59:30Z)
• And/or trade-off in artificial neurons: impact on adversarial robustness [91.3755431537592]
  Presence of sufficient number of OR-like neurons in a network can lead to classification brittleness and increased vulnerability to adversarial attacks. We define AND-like neurons and propose measures to increase their proportion in the network. Experimental results on the MNIST dataset suggest that our approach holds promise as a direction for further exploration.
  arXiv  Detail & Related papers  (2021-02-15T08:19:05Z)
• Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks [15.217367754000913]
  It is increasingly important to obtain models with high robustness that are resistant to adversarial examples. We give preliminary definitions on what adversarial attacks and robustness are. We study frequently-used benchmarks and mention theoretically-proved bounds for adversarial robustness.
  arXiv  Detail & Related papers  (2020-11-03T07:42:53Z)

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.