Performance of Stego Images Calibration Using Advanced Denoising Methods

Abstract

Reliable detection of messages that are unauthorized embedding into cover files, namely digital images, is topical task today. Design of effective stegdetectors is complicated by limited prior information of used adaptive embedding methods that minimize cover image alteration during data hiding. Modern approaches for improving detection accuracy are based on usage either ensembles of stegdetectors, or advanced neural networks, which characterize by high-computation complexity. This makes them inappropriate for real scenarios where fast re-tuning for detection of unknown embedding methods is needed. The alternative approach for stegdetector’s design is based on modification of image pre-processing (calibration) methods, namely selection of methods for detection and extraction weak alteration of cover image features caused by message hiding. Special interest is taken to advanced methods for image denoising due to theirs high “adaptiveness” to statistical features of processed images that provide local processing of image’s regions instead of global ones. The work is devoted to performance analysis of Total Variation Minimization (TVM) technique usage of calibration of stego images formed by adaptive embedding methods. According to obtained results we may conclude that applying of advanced TVM-based denoising methods does not allow considerably improving stegdetector detection accuracy in comparison with usage of standard SPAM model. On the other hand, applying of component analysis, namely Principal Component Analysis, allows considerably improve Matthews Correlation Coefficient (up to 0.3) for stegdetector in case of processing real images from MIRFlickr dataset. This can be explained by “grouping” of cover image alterations caused by message hiding into single component with the smallest energy (singular value) that makes possible its easy removal.