Entropy Based Image Quality Assessment of Stego Images Created by Pulse Coupled Neural Network - Forgac, Radoslav; Ockay, Milos; Krakovsky, Roman (2020 New Trends in Signal Processing (NTSP), 2020)
The paper aims to the evaluation of image quality assessments of stego images based on entropy. Two embedding approaches are compared. The first approach is based on a position matrix, which is generated for each image using the Optimized Model of Pulse Coupled Neural Network (OM-PCNN). The second, so called reference approach, is based on generating the random positions for embedding. The subject of research was to observe the increase in entropy of stego images compared to cover images for both embedding approaches. From the point of view of image steganography, a case with zero change in entropy is considered an ideal result. Experiments have shown that the embedding by OM-PCNN position matrix causes smaller increase in entropy compared to the random embedding. Therefore, the OM-PCNN approach is prerequisite for the lower detectability of the message embedding.