# Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE

Source: [Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE](<https://www.sota2.com/research/paper/68fa6a951e209131cea2911a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:28:26.804Z

Authors: Wenzhao Xiang, Chang Liu, Shibao Zheng

Published: 2021-08-25T03:45:54.000Z

## Abstract

Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm. In practice, un-restricted adversarial attack has raised great concern and presented a new threat to the AI safety. In this paper, we propose a wavelet-VAE structure to reconstruct an input image and generate adversarial examples by modifying the latent code. Different from perturbation-based attack, the modifications of the proposed method are not limited but imperceptible to human eyes. Experiments show that our method can generate high quality adversarial examples on ImageNet dataset.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2108.11032>)

[PDF](<https://arxiv.org/pdf/2108.11032.pdf>)