Related papers: On the Interplay of Convolutional Padding and Adversarial Robustness

On the Interplay of Convolutional Padding and Adversarial Robustness

URL: http://arxiv.org/abs/2308.06612v1
Date: Sat, 12 Aug 2023 17:06:48 GMT
Title: On the Interplay of Convolutional Padding and Adversarial Robustness
Authors: Paul Gavrikov and Janis Keuper
Abstract summary: We show that adversarial attacks often result in perturbation anomalies at the image boundaries, which are the areas where padding is used. We seek an answer to the question of how different padding modes (or their absence) affect adversarial robustness in various scenarios.
Score: 16.306183236605364
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: It is common practice to apply padding prior to convolution operations to preserve the resolution of feature-maps in Convolutional Neural Networks (CNN). While many alternatives exist, this is often achieved by adding a border of zeros around the inputs. In this work, we show that adversarial attacks often result in perturbation anomalies at the image boundaries, which are the areas where padding is used. Consequently, we aim to provide an analysis of the interplay between padding and adversarial attacks and seek an answer to the question of how different padding modes (or their absence) affect adversarial robustness in various scenarios.

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