# Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers

## TL;DR

This paper introduces a black-box attack method for deep image classifiers that maintains high perceptual quality by following a low-distortion path, demonstrating effectiveness in real-world applications.

## Contribution

It proposes a novel low-distortion path approach for black-box attacks that enhances attack efficiency while preserving image quality.

## Key findings

- Effective in fooling classifiers with minimal perceptual distortion
- Applicable to biometric and forensic systems
- Outperforms existing black-box attack methods

## Abstract

Deep neural networks provide unprecedented performance in all image classification problems, taking advantage of huge amounts of data available for training. Recent studies, however, have shown their vulnerability to adversarial attacks, spawning an intense research effort in this field. With the aim of building better systems, new countermeasures and stronger attacks are proposed by the day. On the attacker's side, there is growing interest for the realistic black-box scenario, in which the user has no access to the neural network parameters. The problem is to design efficient attacks which mislead the neural network without compromising image quality. In this work, we propose to perform the black-box attack along a low-distortion path, so as to improve both the attack efficiency and the perceptual quality of the adversarial image. Numerical experiments on real-world systems prove the effectiveness of the proposed approach, both in benchmark classification tasks and in key applications in biometrics and forensics.