A Simple Neural Network for Detection of Various Image Steganography Methods


Mikołaj Płachta and Artur Janicki, Warsaw University of Technology, Poland


This paper addresses the problem of detecting image steganography based in JPEG files. We analyze the detection of the most popular steganographic algorithms: J-Uniward, UERD and nsF5, using DCTR, GFR and PHARM features. Our goal was to find a single neural network model that can best perform detection of different algorithms at different data hiding densities. We proposed a three-layer neural network in Dense-Batch Normalization architecture using ADAM optimizer. The research was conducted on the publicly available BOSS dataset. The best configuration achieved an average detection accuracy of 72 percent.


Steganography, deep machine learning, detection malware, BOSS database, image processing.

Full Text  Volume 12, Number 15