Debaprasad Mukherjee

Chiranjoy Chattopadhyay, Bikramjit Sarkar, Debaprasad Mukherjee

Researchers in the field of DNA-based chaotic cryptography have recently proposed a set of novel and efficient image encryption algorithms. In this paper, we present a comprehensive summary of those techniques, which are available in the literature. The discussion given in this paper is grouped into three main areas. At first, we give a brief sketch of the backbone architecture and the theoretical foundation of this field, based on which all the algorithms were proposed. Next, we briefly discuss the set of image encryption algorithms based on this architecture and categorized them as either encryption or cryptanalyzing techniques. Finally, we present the different evaluation metrics used to quantitatively measure the performance of such algorithms. We also discuss the characteristic differences among these algorithms. We further highlight the potential advances that are needed to improvise the present state-of-the-art image encryption technique using DNA computing and chaos theory. The primary objective of this survey is to provide researchers in the field of DNA computing and chaos theory based image encryption a comprehensive summary of the progress achieved so far and to facilitate them to identify a few challenging future research areas.

Sandip Chakraborty, Jiban Dalal, Bikramjit Sarkar et al.

Exchange of secret keys over public channels based on neural synchronization using a variety of learning rules offer an appealing alternative to number theory based cryptography algorithms. Though several forms of attacks are possible on this neural protocol e.g. geometric, genetic and majority attacks, our survey finds that deterministic algorithms that synchronize with the end-point networks have high time complexity, while probabilistic and population-based algorithms have demonstrated ability to decode the key during its exchange over the public channels. Our survey also discusses queries, heuristics, erroneous information, group key exchange, synaptic depths, etc, that have been proposed to increase the time complexity of algorithmic interception or decoding of the key during exchange. The Tree Parity Machine and its variants, neural networks with tree topologies incorporating parity checking of state bits, appear to be one of the most secure and stable models of the end-point networks. Our survey also mentions some noteworthy studies on neural networks applied to other necessary aspects of cryptography. We conclude that discovery of neural architectures with very high synchronization speed, and designing the encoding and entropy of the information exchanged during mutual learning, and design of extremely sensitive chaotic maps for transformation of synchronized states of the networks to chaotic encryption keys, are the primary issues in this field.