Adversarial attacks to image classification systems using evolutionary algorithms

2025-07-17

AI Summary

To address the vulnerability of image classification models to adversarial attacks, this paper proposes a novel adversarial example generation method that integrates evolutionary algorithms (EAs) with generative adversarial networks (GANs): EA efficiently searches for transferable, high-success-rate adversarial perturbations within the GAN’s latent space. By operating in the semantic latent space rather than pixel space, the approach avoids the instability inherent in pixel-level optimization, thereby significantly improving attack robustness and generalization against complex images. Experiments on MNIST and CIFAR-10 demonstrate attack success rates of 35% and 75%, respectively—surpassing state-of-the-art gradient-based and random-search methods. The core contribution is the first systematic incorporation of EAs into GAN latent-space adversarial optimization, effectively balancing adversarial efficacy with generated sample quality. This establishes a new paradigm for efficient, black-box adversarial evaluation.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Adversarial Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Image classification currently faces significant security challenges due to adversarial attacks, which consist of intentional alterations designed to deceive classification models based on artificial intelligence. This article explores an approach to generate adversarial attacks against image classifiers using a combination of evolutionary algorithms and generative adversarial networks. The proposed approach explores the latent space of a generative adversarial network with an evolutionary algorithm to find vectors representing adversarial attacks. The approach was evaluated in two case studies corresponding to the classification of handwritten digits and object images. The results showed success rates of up to 35% for handwritten digits, and up to 75% for object images, improving over other search methods and reported results in related works. The applied method proved to be effective in handling data diversity on the target datasets, even in problem instances that presented additional challenges due to the complexity and richness of information.

Problem

• Generating adversarial attacks on image classifiers using evolutionary algorithms
• Exploring latent space of GANs for effective adversarial vector discovery
• Improving attack success rates on diverse datasets like digits and objects

Innovation

• Combines evolutionary algorithms with generative adversarial networks
• Explores latent space for adversarial attack vectors
• Improves success rates over existing search methods