In 1998, Paul Kocher showed that when a smart card computes cryptographic algorithms, for signatures or encryption, its consumption or its radiations leak information [1]. The keys or the secrets hidden in the card can then be recovered using a differential measurement based on the intercorrelation function. A lot of silicon manufacturers use desynchronisation countermeasures to defeat power analysis. In this article we detail a new resynchronisation technic. This method can be used to facilitate the use of a neural network to do the code recognition. It becomes possible to reverse engineer a software code automatically. Using data and clock separation methods, we show how to optimize the synchronization using signal processing. Then we compare these methods with watermarking methods for 1D and 2D signal. The very last watermarking detection improvements can be applied to signal processing for smart cards with very few modifications. Bayesian processing is one of the best ways to do Differential Power Analysis, and it is possible to extract a PIN code from a smart card in very few samples. So this article shows the need to continue to set up effective countermeasures for cryptographic processors. Although the idea to use advanced signal processing operators has been commonly known for a long time, no publication explains that results can be obtained. The main idea of differential measurement is to use the cross-correlation of two random variables and to repeat consumption measurements on the processor to be analyzed. We use two processors clocked at the same external frequency and computing the same data. The applications of our design are numerous. Two measurements provide the inputs of a central operator. With the most accurate operator we can improve the signal noise ratio, re-synchronize the acquisition clock with the internal one, or remove jitter. The analysis based on consumption or electromagnetic measurements can be improved using our structure. At first sight the same results can be obtained with only one smart card, but this idea is not completely true because the statistical properties of the signal are not the same. As the two smart cards are submitted to the same external noise during the measurement, it is more easy to reduce the influence of perturbations. This paper shows the importance of accurate countermeasures against differential analysis.
