Перегляд за Автор "Progonov, Dmytro"

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  • Ескіз
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    Analysis of changes the Renyi divergence for pixel brightness distributions by stego images Wiener filtering
    (2018) Progonov, Dmytro
    Counteraction of sensitive information leakage is topical task today. Special interest is taken to early detection of confidential information unauthorized transmission via commonly used communication systems, such as e-mail, data sharing services, social networks etc. Providing a high detection accuracy of hidden messages (stego files) requires usage of computation intensive detection methods, which are based on cover rich models, usage of artificial neural networks etc. For counteraction to mentioned methods there were proposed detector-aware information embedding, e.g. MG, MiPOD algorithms. These embedding methods allows reducing stegdetector performance (probability of stego file detection) by preserving minimum alterations of cover files, such as digital images. For revealing stego images, formed according to detector-aware embedding methods, there is proposed to analyze differences in results of processing cover and stego images with usage of information-theoretic indices, such as chi-squared divergence, spectrum of Renyi divergence. The paper is devoted to performance analysis of usage the Renyi divergence spectrum for revealing differences between results of cover and stego images Wiener filtering. It is shown that preliminary processing (filtering) of stego images allows amplifying small alterations of cover image caused by information hiding even in case of low cover image payload (less than 10%). It is revealed that usage of Renyi divergence spectrum does not allow significantly improving stego image detection accuracy. Applying of chi-squared divergences allows not only improving detection performance, but also determine type of used steganographic algorithm.
  • Ескіз
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    Destruction of stego images formed by adaptive embedding methods with dictionary learning methods
    (Igor Sikorsky Kyiv Polytechnic Institute, 2022) Progonov, Dmytro
    Counteraction to sensitive information leakage that processed by state and private organizations is topical task today. Of special interest are methods for prevention data leakage by usage of hidden (steganographic) communication channels by attackers. Despite wide range of proposed steganalysis methods for detection of embedded messages, theirs performance highly depends on prior information about used embedding methods. As an example, we may mention modern stegdetectors for digital images, which are based on cover rich models and deep convolutional neural networks. Therefore, the stego image destruction methods are widely applied as preventive action. Modern methods for stego image destruction are based on widespread image denoising methods, like median filter and lossy compression. The limitation of such methods is significant changes of image’s statistical features that may disclosure the steganalysis process to attacker. Therefore, development of stego images processing methods that provide reliable destruction of embedded data, and preserving cover image statistical features is needed. The paper is aimed at performance evaluation of applying the novel methods of spectral analysis, namely dictionary learning, for solving this tasks. The obtained results showed limitation of state-of-the-art methods for destruction of stego image formed by adaptive embedding methods, namely considerable changes of image’s statistical parameters. The proposed method allows preserving both minimal changes of a Cover Image (CI) parameters, and ratio of survived bits of embedded message (less than 7%). This makes proposed solution an attractive candidate for reliable destruction of stego images formed by novel embedding methods. However, practical usage of proposed solution requires further improvement of dictionary learning methods, namely decreasing of computation complexity of dictionary forming procedure.
  • Ескіз
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    Statistical stegdetectors performance by message re-embedding
    (Igor Sikorsky Kyiv Polytechnic Institute, 2021) Progonov, Dmytro
    State-of-the-art stegdetectors for digital images are based on pre-processing (calibration) of analyzed image for increasing stego-to-cover ratio. In most cases, the calibration is realized by image processing with enormous set of high-pass filters to obtain good estimation of cover image from the stego one. Nevertheless, the efficiency of this approach significantly depends on careful selection of filters for reliably extraction of cover image alterations that are specific for each embedding method. The selection is non-trivial and laborious operation that is realized today by training of convolutional neural networks, such as Ye-Net, SR-Net to name but a few. The paper is devoted to performance analysis of alternative approach to image calibration, namely message re-embedding into analyzed image. The considered method is aimed to increasing stego-to-cover ratio by amplification of cover image alterations caused by message hiding. The analysis was performed on ALASKA and VISION datasets by usage of stegdetector based on SPAM model of covers. Messages were re-embedded according to state-of-the-art adaptive methods HUGO, S-UNIWARD, MG and MiPOD. Proposed approach allows significantly (up to 20%) decreasing detection error even in case of low payload of cover image (less than 10%) where modern stegdetectors are ineffective.