# Adversarial Test on Learnable Image Encryption

## TL;DR

This paper evaluates the robustness of learnable image encryption against adversarial attacks across multiple scenarios, revealing its potential for privacy preservation with some inherent adversarial resilience.

## Contribution

It introduces an adversarial testing framework for learnable image encryption, highlighting its robustness and vulnerabilities in different key scenarios.

## Key findings

- Learnable image encryption shows some adversarial robustness.
- Network behavior varies across different key scenarios.
- Encryption provides a certain level of privacy protection.

## Abstract

Data for deep learning should be protected for privacy preserving. Researchers have come up with the notion of learnable image encryption to satisfy the requirement. However, existing privacy preserving approaches have never considered the threat of adversarial attacks. In this paper, we ran an adversarial test on learnable image encryption in five different scenarios. The results show different behaviors of the network in the variable key scenarios and suggest learnable image encryption provides certain level of adversarial robustness.