Performance Analysis Of Stego Image Calibration With Usage Of Denoising Autoencoders
Methods for early detection of sensitive information leakage by data transmission in open (public) communication systems have been of special interest. Reliable detection of modified (stego) cover files, like digital images, requires usage of computation-intensive methods of statistical steganalysis, namely covering rich models and deep convolutional neural networks. Necessity of fine-tuning parameters of such methods to minimize detection accuracy for each embedding methods has made fast re-train of stegdetectors in real cases impossible.