Abstract
The aim is to propose basic steganalytical tool that can use multiple methods of analysis. We describe two detection methods that were implemented. These methods include improved detection capability than conventional steganalytical tools thanks to use of artificial neural network and several other innovative improvements. In our work is important to understand the behavior of the targeted steganography algorithm. Then we can use its weaknesses to increase the detection capability. We analyze prepared stegogrammes by application of several conventional algorithms such as image difference. Then we can determine where are the most suitable areas of image for embedding the message by steganography algorithm.
Two of our plug-ins are focused on steganography algorithms Steghide, OutGuess2.0 and F5. These algorithms are open source and easy accessible, so the risk of their abuse is high. We use several approaches, such as calibration process and blockiness calculation to detect the presence of steganography message in suspected image. Calibration process is designed for creation of calibration image, that represents the original cover work and for comparison to suspected image. Blockiness calculation serves us as a statistical metric that react to the presence of secret message. Next we deploy the artificial neural network to improve detection capability.
Second plug-in utilizes a detection method that is based on analysis of inner structures of JPEG format. This detection method uses overall quality calculation based on quantization tables and Huffman coding table. These informations are processed by neural network that is able to decide whatever the suspicious file contains embedded data and which steganography algorithm was used to create this file with tested confidence larger than 93% and for detection capability up to 99%.
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The following grant is acknowledged for the financial support provided for this research: Technology Agency of the Czech Republic - TACR - TF01000091.
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Hendrych, J., Kunčický, R., Ličev, L. (2018). New Approach to Steganography Detection via Steganalysis Framework. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds) Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17). IITI 2017. Advances in Intelligent Systems and Computing, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-319-68321-8_51
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DOI: https://doi.org/10.1007/978-3-319-68321-8_51
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