Résumé
In Side-Channel Analysis (SCA), several papers have shown that neural networks could be trained to efficiently extract sensitive information from implementations running on embedded devices. This paper introduces a new tool called Gradient Visualization that aims to proceed a post-mortem information leakage characterization after the successful training of a neural network. It relies on the computation of the gradient of the loss function used during the training. The gradient is no longer computed with respect to the model parameters, but with respect to the input trace components. Thus, it can accurately highlight temporal moments where sensitive information leaks. We theoretically show that this method, based on Sensitivity Analysis, may be used to efficiently localize points of interest in the SCA context. The efficiency of the proposed method does not depend on the particular countermeasures that may be applied to the measured traces as long as the profiled neural network can still learn in presence of such difficulties. In addition, the characterization can be made for each trace individually. We verified the soundness of our proposed method on simulated data and on experimental traces from a public side-channel database. Eventually we empirically show that the Sensitivity Analysis is at least as good as state-of-the-art characterization methods, in presence (or not) of countermeasures.