# Adaptive Gradient for Adversarial Perturbations Generation

## TL;DR

This paper introduces an adaptive gradient method designed to improve the generation of adversarial perturbations, enhancing the robustness and attack effectiveness against deep neural networks.

## Contribution

The paper proposes a novel adaptive gradient technique specifically tailored for generating more effective adversarial perturbations.

## Key findings

- Improved attack success rates on benchmark datasets
- Enhanced transferability of adversarial examples
- Reduced computational cost for adversarial generation

## Abstract

Deep Neural Networks have achieved remarkable success in computer vision, natural language processing, and audio tasks.

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Source: https://tomesphere.com/paper/1902.01220