Journal of Visual Communication and Image Representation 93, pp.103818 .
ISSN/ISBN: Not available at this time. DOI: 10.1016/j.jvcir.2023.103818
Abstract: Convolutional neural networks (CNNs) are fragile to small perturbations in the input images. These networks are thus prone to malicious attacks that perturb the inputs to force a misclassification. Such slightly manipulated images aimed at deceiving the classifier are known as adversarial images. In this work, we investigate statistical differences between natural images and adversarial ones. More precisely, we show that employing a proper image transformation for a class of adversarial attacks, the distribution of the leading digit of the pixels in adversarial images deviates from Benford’s law. The stronger the attack, the more distant the resulting distribution is from Benford’s law. Our analysis provides a detailed investigation of this new approach that can serve as a basis for alternative adversarial example detection methods that do not need to modify the original CNN classifier neither work on the high-dimensional pixel space for features to defend against attacks.
Bibtex:
@article{,
title = {Benford’s law: What does it say on adversarial images?},
journal = {Journal of Visual Communication and Image Representation},
volume = {93},
pages = {103818},
year = {2023},
issn = {1047-3203},
doi = {https://doi.org/10.1016/j.jvcir.2023.103818},
url = {https://www.sciencedirect.com/science/article/pii/S1047320323000688},
author = {João G. Zago and Eric A. Antonelo and Fabio L. Baldissera and Rodrigo T. Saad},
}
Reference Type: Journal Article
Subject Area(s): Image Processing