adaptive embedding methods

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.

Detection of Stego Images with Adaptively Embedded Data by Component Analysis Methods

Ensuring the effective protection of personal and corporate sensitive data is topical task today. The special interest is taken at sensitive data leakage prevention during files transmission in communication systems. In most cases, these leakages are conducted by usage of advance adaptive steganographic methods. These methods are aimed at minimizing distortions of cover files, such as digital images, during data hiding that negatively impact on detection accuracy of formed stego images.