INFLUENCE OF DIGITAL IMAGES PRELIMINARY NOISING ON STATISTICAL STEGDETECTORS PERFORMANCE
DOI:
https://doi.org/10.15588/1607-3274-2021-1-18Keywords:
digital image steganalysis, adaptive embedding method, Gaussian noise, Poisson noise.Abstract
Context. The problem of sensitive information protection during data transmission in communication systems was considered. The case of reliable detection of stego images formed according to advanced embedding methods was investigated. The object of research is digital images steganalysis of adaptive steganographic methods.
Objective. The goal of the work is performance analysis of statistical stegdetectors for adaptive embedding methods in case of preliminary noising of analyzed image with thermal and shot noises.
Method. The image pre-processing (calibration) method was proposed for improving stego-to-cover ratio for state-of-the-art adaptive embedding methods HUGO, MG and MiPOD. The method is aimed at amplifying negligible changes of cover image caused by message hiding with usage of Gaussian and Poisson noises. The former one is related to influence the thermal noise of chargecoupled device (CCD) based image sensor during data acquisition. The latter one is related to shot noise that originates from stochastic process of electron emission by photons hitting of CCD elements. During the research, parameters of thermal noise were estimated with two-dimensional Wiener filter, while sliding window of size 5·5 pixels was used for parameters evaluation for shot noise.
Results. The dependencies of detection error on cover image payload for advance HUGO, MG and MiPOD embedding methods were obtained. The results were presented for the case of image pre-noising with both Gaussian and Poisson noises, and varying of feature pre-processing methods.
Conclusions. The conducted experiments confirmed effectiveness of proposed approach for image calibration with Poisson noise. Obtained results allow us to recommend linearly transformed features to be used for improving stegdetector performance by natural image processing. The prospects for further research may include investigation usage of special noises, such as fractal noises, for improving stego-to-cover ratio for advanced embedding methods.
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