Deep learning-based side-channel analysis performance heavily depends on the dataset size and the number of instances in each target class. Both small and imbalanced datasets might lead to unsuccessful side-channel attacks. The attack performance can be improved by generating traces synthetically from the obtained data instances instead of collecting them from the target device. Unfortunately, generating the synthetic traces that have characteristics of the actual traces using random noise is a difficult and cumbersome task. This research proposes a novel data augmentation approach based on conditional generative adversarial networks (cGAN) and Siamese networks, enhancing in this way the attack capability. We present a quantitative comparative machine learning-based side-channel analysis between a real raw signal leakage dataset and an artificially augmented leakage dataset. The analysis is performed on the leakage datasets for both symmetric and public-key cryptographic implementations. We also investigate non-convergent networks’ effect on the generation of fake leakage signals using two cGAN based deep learning models. The analysis shows that the proposed data augmentation model results in a well-converged network that generates realistic leakage traces, which can be used to mount deep learning-based side-channel analysis successfully even when the dataset available from the device is not optimal. Our results show potential in breaking datasets enhanced with “faked” leakage traces, which could change the way we perform deep learning-based side-channel analysis.