Improved Security of a Deep Learning-Based Steganography System with Imperceptibility Preservation

Abstract

Since its inception, the steganography system (SS) has continuously evolved and is routinely used for concealing various sensitive data in an imperceptible manner. To attain high performance and a better hiding capacity of the traditional SS, it has become essential to integrate them with diverse modern algorithms, especially those related to artificial intelligence (AI) and deep learning (DL). Based on this fact, we proposed a DL-based SS (DLSS) to extract some significant features (like pixel locations, importance, and proximity to the imperceptibility) from the cover image using a neural network (NN) in a hierarchical form, thus selecting the candidate pixels for embedding afterwards. The pixel weight was expressed in terms of the position, imperceptibility, and its relationship with adjacent pixels to be a stego image. Performance evaluation revealed that the proposed DLSS achieved imperceptibility of 84 dB for images in training mode of a standard dataset.