Adversarial attacks to image classification systems using evolutionary algorithms

• URL: http://arxiv.org/abs/2507.13136v1
• Date: Thu, 17 Jul 2025 13:57:21 GMT
• Title: Adversarial attacks to image classification systems using evolutionary algorithms
• Authors: Sergio Nesmachnow, Jamal Toutouh,
• Abstract summary: This article explores an approach to generate adversarial attacks against image classifiers using a combination of evolutionary algorithms and generative adversarial networks.<n>The proposed approach explores the latent space of a generative adversarial network with an evolutionary algorithm to find vectors representing adversarial attacks.<n>The results showed success rates of up to 35% for handwritten digits, and up to 75% for object images.
• Score: 0.3683202928838613
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Adversarial Attack Against Images Classification based on Generative Adversarial Networks [0.0]
  Adrial attacks on image classification systems have always been an important problem in the field of machine learning.<n>With the popularity of generative adversarial networks, the misuse of fake image technology has raised a series of security problems.<n>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.
  arXiv  Detail & Related papers  (2024-12-21T15:23:34Z)
• Self-Supervised Representation Learning for Adversarial Attack Detection [6.528181610035978]
  Supervised learning-based adversarial attack detection methods rely on a large number of labeled data. We propose a self-supervised representation learning framework for the adversarial attack detection task to address this drawback.
  arXiv  Detail & Related papers  (2024-07-05T09:37:16Z)
• Imperceptible Face Forgery Attack via Adversarial Semantic Mask [59.23247545399068]
  We propose an Adversarial Semantic Mask Attack framework (ASMA) which can generate adversarial examples with good transferability and invisibility. Specifically, we propose a novel adversarial semantic mask generative model, which can constrain generated perturbations in local semantic regions for good stealthiness.
  arXiv  Detail & Related papers  (2024-06-16T10:38:11Z)
• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• Counterfactual Image Generation for adversarially robust and interpretable Classifiers [1.3859669037499769]
  We propose a unified framework leveraging image-to-image translation Generative Adrial Networks (GANs) to produce counterfactual samples. This is achieved by combining the classifier and discriminator into a single model that attributes real images to their respective classes and flags generated images as "fake" We show how the model exhibits improved robustness to adversarial attacks, and we show how the discriminator's "fakeness" value serves as an uncertainty measure of the predictions.
  arXiv  Detail & Related papers  (2023-10-01T18:50:29Z)
• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation [16.109860499330562]
  We introduce an uncertainty-based approach for the detection of adversarial attacks in semantic segmentation. We demonstrate the ability of our approach to detect perturbed images across multiple types of adversarial attacks.
  arXiv  Detail & Related papers  (2023-05-22T08:36:35Z)
• Deviations in Representations Induced by Adversarial Attacks [0.0]
  Research has shown that deep learning models are vulnerable to adversarial attacks. This finding brought about a new direction in research, whereby algorithms were developed to attack and defend vulnerable networks. We present a method for measuring and analyzing the deviations in representations induced by adversarial attacks.
  arXiv  Detail & Related papers  (2022-11-07T17:40:08Z)
• Identification of Attack-Specific Signatures in Adversarial Examples [62.17639067715379]
  We show that different attack algorithms produce adversarial examples which are distinct not only in their effectiveness but also in how they qualitatively affect their victims. Our findings suggest that prospective adversarial attacks should be compared not only via their success rates at fooling models but also via deeper downstream effects they have on victims.
  arXiv  Detail & Related papers  (2021-10-13T15:40:48Z)
• Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks [104.8737334237993]
  We present comprehensive investigations into the vulnerability of deep image-to-image models to adversarial attacks. For five popular image-to-image tasks, 16 deep models are analyzed from various standpoints. We show that unlike in image classification tasks, the performance degradation on image-to-image tasks can largely differ depending on various factors.
  arXiv  Detail & Related papers  (2021-04-30T14:20:33Z)
• MixNet for Generalized Face Presentation Attack Detection [63.35297510471997]
  We have proposed a deep learning-based network termed as textitMixNet to detect presentation attacks. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category.
  arXiv  Detail & Related papers  (2020-10-25T23:01:13Z)

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.