Multi-Datasets Evaluation of GB-RAS Network Based Stegdetectors Robustness to Domain Adaptation Problem

Abstract

Protection of state and commercial critical infrastructures is topical task today. Among known threats, special interest is taken to early detection of hidden (steganographic) channels usage by attackers for unauthorized transmission of sensitive data. These channels are created by modification of the innocuous files, such as digital images, for message hiding and further transmission of modified files. Detection of formed stego files is non-trivial task due to the wide usage by attackers of adaptive embedding methods that preserve low level of cover image distortion during embedding. Modern solutions for stego images detection are based on utilization the novel convolution neural networks for revealing weak alterations of cover’s features during message hiding. Training of such networks is performed on fixed set of standard image databases, such as BOSS, ALASKA etc. Despite high detection accuracy of pre-trained stegdetectors, theirs performance “in the wild” on natural images remains an open question. The work is devoted to performance analysis of advanced GB-Ras convolutional network based stegdetector for adaptive embedding methods detection on natural images presented in various datasets. Obtained results proved the effectiveness of neural network applying for mitigation with domain adaptation problem for modern stegdetectors. The GB-Ras model allows improving detection accuracy of stego images for standard ALASKA (up to 6% for for S-UNIWARD embedding methods) and VISION (up to 13% for S-UNIWARD and to 9% for MG embedding methods) datasets in comparison with cover rich models based stegdetectors. Nevertheless, the performance of considered GB-Ras based stegdetector for high quality images from MIRFlickr dataset is conceded to maxSRMd2 rich model based stegdetector (up to 11% for S-UNIWARD and 12% for MG embedding methods). Therefore, considered GB-Ras network may be used as a promising candidate for further design of novel stegdetectors that are robust to domain adaptation problem.