An Approximated Gradient Sign Method Using Differential Evolution for Black-Box Adversarial Attack

Chao Li

doi:10.1109/TEVC.2022.3151373

2022-10-01

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DOI：10.1109/TEVC.2022.3151373

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摘要

En 中文

Recent studies show that deep neural networks are vulnerable to adversarial attacks in the form of subtle perturbations to the input image, which leads the model to output wrong prediction. Such an attack can easily succeed by the existing white-box attack methods, where the perturbation is calculated based on the gradient of the target network. Unfortunately, the gradient is often unavailable in the real-world scenarios, which makes the black-box adversarial attack problems practical and challenging. In fact, they can be formulated as high-dimensional black-box optimization problems at the pixel level. Although evolutionary algorithms are well known for solving black-box optimization problems, they cannot efficiently deal with the high-dimensional decision space. Therefore, we propose an approximated gradient sign method using differential evolution (DE) for solving black-box adversarial attack problems. Unlike most existing methods, it is novel that the proposed method searches the gradient sign rather than the perturbation by a DE algorithm. Also, we transform the pixel-based decision space into a dimension-reduced decision space by combining the pixel differences from the input image to neighbor images, and two different techniques for selecting neighbor images are introduced to build the transferred decision space. In addition, six variants of the proposed method are designed according to the different neighborhood selection and optimization search strategies. Finally, the performance of the proposed method is compared with a number of the state-of-the-art adversarial attack algorithms on CIFAR-10 and ImageNet datasets. The experimental results suggest that the proposed method shows superior performance for solving black-box adversarial attack problems, especially nontargeted attack problems.

Keyword:

Perturbation methods

Optimization

Convolutional neural networks

Evolutionary computation

Technological innovation

Predictive models

Neural networks

Adversarial attack

black box

deep neural networks (DNNs)

differential evolution (DE)