An automated method for cryptanalysis of DFT-based analog speech scramblers is presented through statistical estimation treatments. In the proposed system, the ciphertext only attack is formulated as a combinatorial optimization problem leading to a search for the most likely key estimate. For greater efficiency, we also explore the benefits of genetic algorithm to develop an estimation method which takes into account the doubly stochastic characteristics of the underlying keyspace. Simulation results indicate that the global explorative properties of genetic algorithms make them very effective at estimating the most likely permutation and by using this estimate significant amount of the intelligibility can be recovered from the ciphertext following the attack on DFT-based speech scramblers.

An approach to minimum mean-squared error (MMSE) decoding for vector quantization over channels with memory is presented. The decoder is based on the Gilbert channel model that allows the exploitation of both intra- and inter-block correlation of bit error sequences. We also develop a recursive algorithm for computing the a posteriori probability of a transmitted index sequence, and illustrate its performance in quantization of Gauss-Markov sources under noisy channel conditions.