# mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks

Source: [mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks](<https://www.sota2.com/research/paper/68fa6d841e209131cea3a4d2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:36:22.217Z

Authors: Hai Shu, Ronghua Shi, Qiran Jia, Hongtu Zhu, Ziqi Chen

Published: 2020-06-05T05:42:58.000Z

## Abstract

Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images. To improve adversarial image generation for DNNs, we develop a novel method, called mFI-PSO, which utilizes a Manifold-based First-order Influence measure for vulnerable image and pixel selection and the Particle Swarm Optimization for various objective functions. Our mFI-PSO can thus effectively design adversarial images with flexible, customized options on the number of perturbed pixels, the misclassification probability, and the targeted incorrect class. Experiments demonstrate the flexibility and effectiveness of our mFI-PSO in adversarial attacks and its appealing advantages over some popular methods.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.03243>)

[PDF](<https://arxiv.org/pdf/2006.03243.pdf>)