Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients

TL;DR

This paper introduces a novel method for generating minimal adversarial perturbations by integrating adaptive gradients, aiming to improve the efficiency and subtlety of adversarial attacks on deep neural networks.

Contribution

It proposes an innovative approach that combines adaptive gradient techniques to produce minimal and effective adversarial perturbations.

Findings

The method achieves lower perturbation magnitudes compared to existing techniques.

It demonstrates high success rates in fooling neural networks with minimal changes.

The approach enhances understanding of model vulnerabilities.

Abstract

Deep neural networks are easily fooled high confidence predictions for adversarial samples