Potential adversarial samples for white-box attacks

Abstract

Deep convolutional neural networks can be highly vulnerable to small perturbations of their inputs, potentially a major issue or limitation on system robustness when using deep networks as classifiers. In this paper we propose a low-cost method to explore marginal sample data near trained classifier decision boundaries, thus identifying potential adversarial samples. By finding such adversarial samples it is possible to reduce the search space of adversarial attack algorithms while keeping a reasonable successful perturbation rate. In our developed strategy, the potential adversarial samples represent only 61% of the test data, but in fact cover more than 82% of the adversarial samples produced by iFGSM and 92% of the adversarial samples successfully perturbed by DeepFool on CIFAR10.

References in corpus (5)

• Explaining and Harnessing Adversarial Examples
• ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
• Deep Learning using Linear Support Vector Machines
• Predicting the Generalization Gap in Deep Networks with Margin Distributions
• On the Geometry of Adversarial Examples

Cited by in corpus (4)

• Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation
• mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks
• Noise Optimization for Artificial Neural Networks
• Feature Importance Guided Attack: A Model Agnostic Adversarial Attack