Shift-invariance Robustness of Convolutional Neural Networks in Side-channel Analysis

Marina Krcek, Lichao Wu, Guilherme Perin, Stjepan Picek

Abstract: Convolutional neural networks (CNNs) offer unrivaled performance in profiling side-channel analysis. This claim is corroborated by numerous results where CNNs break targets protected with masking and hiding countermeasures. One hiding countermeasure is commonly investigated in related works - desynchronization (misalignment). The conclusions usually state that CNNs can break desynchronization as they are shift-invariant. This paper investigates that claim in more detail and reveals that the situation is more complex. While CNNs have certain shift-invariance, it is insufficient for commonly encountered scenarios in deep learning-based side-channel analysis. We propose to use data augmentation to improve the shift-invariance and, in a more powerful version, ensembles of data augmentation. Our results show the proposed techniques work very well and improve the attack significantly, even for an order of magnitude.