A Machine Learning based Reversible Data Hiding Scheme in Encrypted Images using Fibonacci Transform

Technological advancements and digitalization have made the life of humankind simple but at the same time imposing many challenges. As information started bursting across the internet, information management and security became major concerns. Recently, researchers have been focusing on a hot topic called reversible data hiding (RDH). RHD secures the data by covering it within another medium. It allows the recovery of the medium and hidden information on the receiver side without any loss. This work discloses a high capacity RDH scheme in the encrypted image with a Fibonacci transform image scrambling algorithm for data hiding and a convolutional neural network (CNN) based recovery. It follows a block-wise embedding process, embedding (n + 1) bits within a block of size 2
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while n > 1. The proposed scheme is tested on the USC-SIPI image data set from the University of Southern California and has resulted in an improved embedding rate compared to the existing Arnold transform-based RDH and many other well-acknowledged RDH schemes.