# Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks

## TL;DR

This paper examines how vulnerable deep learning-based image super-resolution methods are to adversarial attacks, revealing significant weaknesses and analyzing various attack transferability and feasibility aspects.

## Contribution

It provides the first comprehensive analysis of the robustness of super-resolution methods against adversarial attacks, both theoretically and experimentally.

## Key findings

- State-of-the-art methods are highly vulnerable to adversarial attacks.
- Different super-resolution methods exhibit varying robustness levels.
- Transferability and targeted attack feasibility are analyzed.

## Abstract

Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many computer vision applications. This paper investigates the robustness of deep learning-based super-resolution methods against adversarial attacks, which can significantly deteriorate the super-resolved images without noticeable distortion in the attacked low-resolution images. It is demonstrated that state-of-the-art deep super-resolution methods are highly vulnerable to adversarial attacks. Different levels of robustness of different methods are analyzed theoretically and experimentally. We also present analysis on transferability of attacks, and feasibility of targeted attacks and universal attacks.