Adversarial Attack Against Images Classification based on Generative Adversarial Networks

• URL: http://arxiv.org/abs/2412.16662v2
• Date: Tue, 24 Dec 2024 17:21:50 GMT
• Title: Adversarial Attack Against Images Classification based on Generative Adversarial Networks
• Authors: Yahe Yang,
• Abstract summary: Adrial attacks on image classification systems have always been an important problem in the field of machine learning. With the popularity of generative adversarial networks, the misuse of fake image technology has raised a series of security problems. This work proposes a novel adversarial attack method, aiming to gain insight into the weaknesses of the image classification system and improve its anti-attack ability.
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• Abstract: Adversarial attacks on image classification systems have always been an important problem in the field of machine learning, and generative adversarial networks (GANs), as popular models in the field of image generation, have been widely used in various novel scenarios due to their powerful generative capabilities. However, with the popularity of generative adversarial networks, the misuse of fake image technology has raised a series of security problems, such as malicious tampering with other people's photos and videos, and invasion of personal privacy. Inspired by the generative adversarial networks, this work proposes a novel adversarial attack method, aiming to gain insight into the weaknesses of the image classification system and improve its anti-attack ability. Specifically, the generative adversarial networks are used to generate adversarial samples with small perturbations but enough to affect the decision-making of the classifier, and the adversarial samples are generated through the adversarial learning of the training generator and the classifier. From extensive experiment analysis, we evaluate the effectiveness of the method on a classical image classification dataset, and the results show that our model successfully deceives a variety of advanced classifiers while maintaining the naturalness of adversarial samples.

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