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Related Experiment Video

Updated: Aug 5, 2025

Visualizing Visual Adaptation

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Published on: April 24, 2017

Image Adversarial Example Generation Method Based on Adaptive Parameter Adjustable Differential Evolution.

Zhiyi Lin¹, Changgen Peng¹, Weijie Tan^1,2

¹State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China.

Entropy (Basel, Switzerland)

|March 29, 2023

Summary

This summary is machine-generated.

This study introduces a novel adaptive differential evolution method for generating adversarial examples in image recognition. The technique achieves high attack success rates with minimal pixel modifications, outperforming existing methods.

Keywords:

adaptive differential evolution adversarial example generation image recognition neural network

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Adversarial attacks on neural networks are a growing concern.
Generating effective adversarial examples with minimal changes is challenging.

Purpose of the Study:

To propose a novel adversarial example generation method.
To achieve high attack success rates with minimal pixel modifications.

Main Methods:

Adaptive parameter adjustable differential evolution algorithm.
Dynamic adjustment of control parameters and operation strategies.
Searching for optimal pixel perturbations.

Main Results:

Generated adversarial examples with high success rates and few pixel modifications.
Outperformed conventional differential evolution-based One Pixel Attack.
Required significantly less perturbation than global/local attacks.
Demonstrated increased resistance to perception and detection.

Conclusions:

The proposed adaptive differential evolution method is effective for generating adversarial examples.
The method offers a superior balance between attack success and perturbation magnitude.
It presents a more robust adversarial attack strategy for image recognition models.