# 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.

## Key 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

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Source: https://tomesphere.com/paper/1904.06186