@inproceedings{9effdec440a6448f9b60e5f934ba7dd2,
title = "Defense-Friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty",
abstract = "Dataset bias is a problem in adversarial machine learning, especially in the evaluation of defenses. An adversarial attack or defense algorithm may show better results on the reported dataset than can be replicated on other datasets. Even when two algorithms are compared, their relative performance can vary depending on the dataset. Deep learning offers state-of-the-art solutions for image recognition, but deep models are vulnerable even to small perturbations. Research in this area focuses primarily on adversarial attacks and defense algorithms. In this paper, we report for the first time, a class of robust images that are both resilient to attacks and that recover better than random images under adversarial attacks using simple defense techniques. Thus, a test dataset with a high proportion of robust images gives a misleading impression about the performance of an adversarial attack or defense. We propose three metrics to determine the proportion of robust images in a dataset and provide scoring to determine the dataset bias. We also provide an ImageNet-R dataset of 15000+ robust images to facilitate further research on this intriguing phenomenon of image strength under attack. Our dataset, combined with the proposed metrics, is valuable for unbiased benchmarking of adversarial attack and defense algorithms. {\textcopyright} 2021 IEEE.",
author = "Camilo Pestana and Wei Liu and David Glance and Ajmal Mian",
year = "2021",
month = jan,
day = "5",
doi = "10.1109/WACV48630.2021.00060",
language = "English",
series = "Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "556--565",
booktitle = "Conference Proceedings 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)",
address = "United States",
note = "2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 ; Conference date: 05-01-2021 Through 09-01-2021",
}