Deep Learning-Based Coverless Image Steganography on Medical Images Shared via Cloud

Abstract

Coverless image steganography is an approach for creating images with intrinsic colour and texture information that contain hidden secret information. Recently, generative adversarial networks’ (GANs) deep learning transformers have been used to generate secret hidden images. Although it has been proven that this approach is resistant to steganalysis attacks, it modifies critical information in the images which makes the images not suitable for applications like disease diagnosis from medical images shared over cloud. The colour and textural modification introduced by GANs affects the feature vector which is extracted from certain image regions and used for disease diagnosis. To solve this problem, this work proposes an attention-guided GAN which transforms images only in certain regions and retains the originality of images in certain regions. Due to this, there is not much distortion to features and disease classification accuracy.