  Side-Channel Attacks (SCAs) are a powerful method to attack implementations
of cryptographic algorithms. State-of-the-art techniques such as template
attacks and stochastic models usually require a lot of manual preprocessing and
feature extraction by the attacker. Deep Learning (DL) methods have been
introduced to simplify SCAs and simultaneously lowering the amount of required
side-channel traces for a successful attack. However, the general success of DL
is largely driven by their capability to classify images, a field in which they
easily outperform humans. In this paper, we present a novel technique to
interpret 1D traces as 2D images. We show and compare several techniques to
transform power traces into images, and apply these on different
implementations of the Advanced Encryption Standard (AES). By allowing the
neural network to interpret the trace as an image, we are able to significantly
reduce the number of required attack traces for a correct key guess.We also
demonstrate that the attack efficiency can be improved by using multiple 2D
images in the depth channel as an input. Furthermore, by applying image-based
data augmentation, we show how the number of profiling traces is reduced by a
factor of 50 while simultaneously enhancing the attack performance. This is a
crucial improvement, as the amount of traces that can be recorded by an
attacker is often very limited in real-life applications.
