Securing Digital Forensic Data Using Neural Networks, Elephant Herd Optimization and Complex Sequence Techniques

Every field is becoming digitalized to improve their respective performance. Digital forensics is a rapidly developing field. This paper purely focuses on securing fingerprint data in an efficient manner as these fingerprints can be a vital clue in some of the cases. To protect a fingerprint by ensuring confidentiality and integrity, this paper proposes a novel encryption scheme based on neural networks, elephant herd optimization (EHO) and combination of some sequences like Fractional Brownian Motion (FBM), Cellular Automata, Modified Logistic Map and Hermite Polynomials. It comprises of two scrambling and two substitution techniques. First, the image is scrambled using EHO. Secondly, the image will be scrambled using FBM. Thirdly, the image will be encrypted by modifying the pixel values using the values obtained from the complex sequences. Finally, neural network is used to generate a key and encryption is carried out by adding the key to the original pixel based on position based (row) value modulo 256 and XNORs the output with position based (column) value for every pixel. The proposed algorithm is subjected to various experimental test and is proven to be robust and secure and resisting attacks.