Analyzing the accuracy of detecting steganograms formed by adaptive steganographic methods when using artificial neural networks

Abstract

This paper reports a comparative analysis of accuracy in the detection of steganograms formed according to adaptive steganographic methods, using steganography detectors based on common and specialized types of artificial neural networks. The results of the review of modern convolutional neural networks applied for the tasks of digital image stegoanalysis have established that the accuracy of operating the steganography detectors based on these networks is significantly compromised when processing image packets characterized by a significant variability of statistical parameters. The performance accuracy of steganography detectors based on the modern statistical model of container images maxSRMd2 has been investigated, as well as on the latest convolutional and «hybrid» artificial neural networks, in particular, GB-Ras and ASSAF networks, when detecting steganograms formed according to the adaptive steganographic methods HUGO and MiPOD. It was established that the use of the statistical model maxSRMd2 makes it possible to significantly (up to 30 %) improve the accuracy of steganogram detection in the case of analyzing those images that are characterized by a high level of natural noise. It was found that the use of the ASSAF network makes it possible to significantly (up to 35 %) reduce an error of steganogram detection compared to current steganography detectors based on the GB-Ras network and the maxSRMd2 statistical model. It was determined that the high accuracy of the ASSAF network-based steganography detector is maintained even in the most difficult case of image processing with high noise and poor filling of the container image with stegodata (less than 10 %). The results reported here are of theoretical interest for designing high-precision steganography detectors capable of working under conditions of high variability in image parameters.