Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metrics

• URL: http://arxiv.org/abs/2201.05071v1
• Date: Mon, 10 Jan 2022 12:54:45 GMT
• Title: Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metrics
• Authors: Haya Brama, Lihi Dery, Tal Grinshpoun
• Abstract summary: We present two metrics which are specifically designed to measure the effect of attacks, or the recovery effect of defenses, on the output of neural networks in classification tasks. Inspired by the normalized discounted cumulative gain and the reciprocal rank metrics used in information retrieval literature, we treat the neural network predictions as ranked lists of results. Compared to the common classification metrics, our proposed metrics demonstrate superior informativeness and distinctiveness.
• Score: 6.6389732792316
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The problem of attacks on neural networks through input modification (i.e., adversarial examples) has attracted much attention recently. Being relatively easy to generate and hard to detect, these attacks pose a security breach that many suggested defenses try to mitigate. However, the evaluation of the effect of attacks and defenses commonly relies on traditional classification metrics, without adequate adaptation to adversarial scenarios. Most of these metrics are accuracy-based, and therefore may have a limited scope and low distinctive power. Other metrics do not consider the unique characteristics of neural networks functionality, or measure the effect of the attacks indirectly (e.g., through the complexity of their generation). In this paper, we present two metrics which are specifically designed to measure the effect of attacks, or the recovery effect of defenses, on the output of neural networks in multiclass classification tasks. Inspired by the normalized discounted cumulative gain and the reciprocal rank metrics used in information retrieval literature, we treat the neural network predictions as ranked lists of results. Using additional information about the probability of the rank enabled us to define novel metrics that are suited to the task at hand. We evaluate our metrics using various attacks and defenses on a pretrained VGG19 model and the ImageNet dataset. Compared to the common classification metrics, our proposed metrics demonstrate superior informativeness and distinctiveness.

Related papers

• Rethinking Targeted Adversarial Attacks For Neural Machine Translation [56.10484905098989]
  This paper presents a new setting for NMT targeted adversarial attacks that could lead to reliable attacking results. Under the new setting, it then proposes a Targeted Word Gradient adversarial Attack (TWGA) method to craft adversarial examples. Experimental results demonstrate that our proposed setting could provide faithful attacking results for targeted adversarial attacks on NMT systems.
  arXiv  Detail & Related papers  (2024-07-07T10:16:06Z)
• A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks [52.09243852066406]
  Adversarial Converging Time Score (ACTS) measures the converging time as an adversarial robustness metric. We validate the effectiveness and generalization of the proposed ACTS metric against different adversarial attacks on the large-scale ImageNet dataset.
  arXiv  Detail & Related papers  (2023-10-10T09:39:38Z)
• Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection [22.99930028876662]
  Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. Current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system. We propose a simple and light-weight detector, which leverages recent findings on the relation between networks' local intrinsic dimensionality (LID) and adversarial attacks.
  arXiv  Detail & Related papers  (2022-12-13T17:51:32Z)
• 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)
• On Trace of PGD-Like Adversarial Attacks [77.75152218980605]
  Adversarial attacks pose safety and security concerns for deep learning applications. We construct Adrial Response Characteristics (ARC) features to reflect the model's gradient consistency. Our method is intuitive, light-weighted, non-intrusive, and data-undemanding.
  arXiv  Detail & Related papers  (2022-05-19T14:26:50Z)
• Efficient and Robust Classification for Sparse Attacks [34.48667992227529]
  We consider perturbations bounded by the $ell$--norm, which have been shown as effective attacks in the domains of image-recognition, natural language processing, and malware-detection. We propose a novel defense method that consists of "truncation" and "adrial training" Motivated by the insights we obtain, we extend these components to neural network classifiers.
  arXiv  Detail & Related papers  (2022-01-23T21:18:17Z)
• 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)
• Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection [16.010654200489913]
  This paper proposes a new defense against neural network backdooring attacks. It is based on the intuition that the feature extraction layers of a backdoored network embed new features to detect the presence of a trigger. To detect backdoors, the proposed defense uses two synergistic anomaly detectors trained on clean validation data.
  arXiv  Detail & Related papers  (2020-11-04T20:33:51Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)
• Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification [21.582072216282725]
  Machine learning systems and, specifically, automatic speech recognition (ASR) systems are vulnerable to adversarial attacks. In this paper, we focus on hybrid ASR systems and compare four acoustic models regarding their ability to indicate uncertainty under attack. We are able to detect adversarial examples with an area under the receiving operator curve score of more than 0.99.
  arXiv  Detail & Related papers  (2020-05-24T19:31:02Z)

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.