A Review of Color Image Steganalysis in the Transform Domain
Nour Mohamed, Tamer Farouk Rabie, Ibrahim Kamel · 2020
Image based applications are being utilized in many platforms nowadays such as medical applications, smart cities, social media, etc. The security of these applications is of great importance. Advanced steganography techniques can hide messages, possibly malicious, in innocent cover images. Steganography is a class of data hiding techniques that deal with concealing the existence of secret communication between two entities. Image steganalysis, the reverse of steganography, is concerned with detecting the existence of stego images and therefore exposing the secret communication between these entities. It can be used in police systems and other fields. Conventional steganalysis is divided into two distinct phases; manual feature extraction and classification employing either Ensemble Classifiers or Support Vector Machines. With the evolution of Deep Learning techniques, combining these two phases has become feasible and yields even better results than conventional steganalysis. In this paper, a review on image based steganalysis schemes in the transform domain using colored images is presented, and the evaluation metrics and datasets used are discussed.