RADIOELECTRONIC AND COMPUTER SYSTEMS

It is substantiated that steganographic systems should be used to ensure the protection of special information resources in conditions of its prompt delivery. Here, steganographic technologies are an integral part of complex information protection systems. Simultaneously, for steganographic systems, there is a contradiction between the density of embedded data and level of information compaction of video container (level of reduction of volume bit volume of compact presented video image concerning bit volume of an initial video image). It leads to the fact that under the conditions of the required quality (reliability) of digital video information, the bit rate level of the covert channel is insufficient. Consequently, the scientific-applied problem concerns the necessity to increase the integrity (the level of correspondence of the hidden information before its embedding in a video container and after its extraction) and bit rate of the hidden channel of special information transmission. It is relevant. The solution of the described problem in the field of application of steganographic transformations can be realized based on the application of two different approaches. The first approach is based on methods of direct message embedding. But this approach is characterized by introducing distortions in the video images used as a container. Therefore, changes in structural and statistical patterns in the syntactic description of the video container happen. It reduces the potential for video container compaction. The second approach to creating steganographic transformation methods is based on information hiding using indirect embedding technique. Here, the embedding process exploits the functional dependency between the elements of the video container and the elements of the embedded message. Setting a specific dependency between the elements in the video container corresponds to the embedded element with a value of "0" or "1". However, the existing indirect steganographic transformation methods have a disadvantage. It consists of an insufficient value of embedded data density. To eliminate these disadvantages, it is proposed to develop an approach that allows using not only psychovisual but also structural redundancy of video container for concealment. Therefore, the research objective of this paper is to develop a method for indirect information withholding in the video container compression process to increase the bit rate of the hidden message channel. In the process of research, a steganographic multiagent system is constructed, which allows embedding hidden message elements without loss of information based on the indirect approach by modifying the active bases of the multiagent basis considering their uncertainty. To select transformants (data sets) as containers for information embedding, the requirement of the existence of a base system with all active bases is taken into account. The number of embedded bits of the hidden message is equal to the number of active bases in the base system of the multiadic space. Because of the made experiments, the following results have been received: in the process of embedding messages based on the created method distortions in a video container is not brought; for the created method the additional increase in the hidden channel bit rate in average 5 … 7 times are reached.

image compressing; information security; steganographic transformation; multiadic basis; modification of basis system; coding; compression; video image; the least significant bit; relative replacement

JPEG Privacy & Security Abstract and Executive Summary, 2015. Available at: https://jpeg.org/items/20150910_privacy_security_summary.html. (accessed 7.06.2021).

Sharma, R., Bollavarapu, S. Data Security using Compression and Cryptography Techniques. International Journal of Computer Applications, 2015, vol. 117, no. 14, pp. 15-18. DOI: 10.5120/20621-3342.

Announcing the ADVANCED ENCRYPTION STANDARD (AES), Federal Information Processing Standards Publication 197, 2001. 51 p.

Wang, S., Zhang, X., Liu, X., Zhang, J., Ma, S., Gao, W. Utility Driven Adaptive Preprocessing for Screen Content Video Compression. IEEE Transactions on Multimedia, 2017, vol. 19, no. 3, pp. 660-667.

Gonzales, R. C., Woods, R. E. Digital image processing. Prentice Inc. Upper Saddle River, 2002. 779 p.

Dong, W., Wang, J. JPEG Compression Forensics against Resizing. IEEE Trustcom/BigDataSE/IвSPA, Tianjin, China, 2016, pp. 1001-1007. DOI: 10.1109/TrustCom.2016.0168.

Richter, T. Error Bounds for HDR Image Coding with JPEG XT. Data Compression Conference (DCC), 2017, pp. 122-130. DOI: 10.1109/DCC.2017.7.

Xiao, W., Wan, N.A., Hong and Chen, X. A Fast JPEG Image Compression Algorithm Based on DCT. IEEE International Conference on Smart Cloud (SmartCloud), 2020, pp. 106-110. DOI: 10.1109/SmartCloud49737. 2020.00028.

Rippel, O. et al. Learned Video Compression. IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 3453-3462. DOI: 10.1109/ICCV.2019.00355.

Rivest, R. L., Shamir, A., Adleman, L. M. A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 1978, vol. 21, iss. 2, pp. 120-126. DOI: 10.1145/359340.359342.

Wang, X., Xiao, J., Hu, R., Wang, Z. Cruise UAV Video Compression Based on Long-Term Wide-Range Background. Data Compression Conference (DCC), 2017, pp. 466-467. DOI: 10.1109/DCC.2017.71.

Naor, M., Shamir, A. Visual Cryptography. Proceedings of the Advances in Cryptology – EUROCRYPT’94. Lecture Notes in Computer Science, 1995, vol. 950, pp. 1-12. DOI: 10.1007/bfb0053419.

Djelouah, A., Campos, J., Schaub-Meyer, S., Schroers, C. Neural Inter-Frame Compression for Video Coding. IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 6420-6428. DOI: 10.1109/ICCV.2019.00652.

Narmatha, C., Manimegalai, P., Manimurugan, S. A LS-compression scheme for grayscale images using pixel based technique. International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT), 2017, pp. 1-5. DOI: 10.1109/IGEHT.2017.8093980.

Alam, M. A., Faster Image Compression Technique Based on LZW Algorithm Using GPU Parallel Processing. Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2018, pp. 272-275, DOI: 10.1109/ICIEV.2018.8640956.

Chen, T.-H., Wu, Ch.-S. Efficient multi-secret image sharing based on Boolean operation. Signal Processing, 2011, vol. 91, iss. 1, pp. 90-97. DOI: 10.1016/j.sigpro.2010.06.012.

Deshmukh, M., Nain, N., Ahmed, M. An (n, n)-Multi Secret Image Sharing Scheme Using Boolean XOR and Modular Arithmetic. IEEE 30 th International Conference on Advanced Information Networking and Applications (AINA), 2016, pp. 690-697. DOI: 10.1109/aina.2016.56.

Bui, V., Chang, L., Li, D., Hsu, L., Chen, M. Comparison of lossless video and image compression codecs for medical computed tomography datasets. IEEE International Conference on Big Data (Big Data), 2016, pp. 3960-3962. DOI: 10.1109/BigData.2016.7841075.

Yang, Ch.-N., Chen, Ch.-H., Cai, S.-R. Enhanced Boolean-based multi secret image sharing scheme. Journal of Systems and Software, 2016, vol. 116, pp. 22-34. DOI: 10.1016/j.jss.2015.01.031.

Watanabe, O., Uchida, A., Fukuhara, T., Kiya, H. An Encryption-then-Compression system for JPEG 2000 standard. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, pp. 1226-1230, DOI: 10.1109/ICASSP.2015.7178165.

Zhou, J., Liu, X., Au, O. C., Tang, Y. Y. Designing an Efficient Image Encryption-Then-Compression System via Prediction Error Clustering and Random Permutation. IEEE Transactions on Information Forensics and Security, 2014, vol. 9, no. 1, pp. 39-50. DOI: 10.1109/TIFS.2013.2291625.

Lekakh, A., Belikova, T., Dovbenko, O., Dodukh, O. Method of Increasing the Capacity of Information Threat Detection Filters in Modern Information and Communication Systems. IEEE 3rd International Conference on Advanced Information and Communications Technologies (AICT 2019), 2019, pp. 426-429. DOI: 10.1109/AIACT.2019.8847754.

Barannik, Valeriy. Fast Coding of Irregular Binary Binomial Numbers with a Set Number of Units Series. IEEE 2nd International Conference on Advanced Trends in Information Theory (IEEE ATIT 2020), 2020, pp. 29-33. DOI: 10.1109/ATIT50783.2020.9349356.

Information technology – JPEG 2000 image coding system: Secure JPEG 2000, International Standard ISO/IEC 15444-8, ITU-T Recommendation T.807, 2007. 108 p.

Wang, S., Kim, S. M., Yin, Z., & He, T. Encode when necessary: Correlated network coding under unreliable wireless links. ACM Transactions on Sensor Networks, 2017, vol. 13(1). DOI: 10.1145/3023953.

Honda, T., Murakami, Y., Yanagihara, Y., Kumaki, T., Fujino, T. Hierarchical image-scrambling method with scramble-level controllability for privacy protection. IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS), 2013, pp. 1371-1374. DOI: 10.1109/MWSCAS.2013.6674911.

Wu, H., Sun, X., Yang, J., Zeng, W., Wu, F. Lossless Compression of JPEG Coded Photo Collections. IEEE Transactions on Image Processing, 2016, vol. 25, no. 6, pp. 2684-2696. DOI: 10.1109/TIP.2016.2551366.

Yuan, L., Korshunov, P., Ebrahimi, T. Secure JPEG Scrambling enabling Privacy in Photo Sharing. 11 th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2015, pp. 1-6. DOI: 10.1109/FG.2015.7285022.

Belikova, T. Decoding Method of Information-Psychological Destructions in the Phonetic Space of Information Resources. 2nd IEEE International Conference on Advanced Trends in Information Theory (IEEE ATIT 2020), 2020, pp. 87–91. DOI: 10.1109/ATIT50783.2020.9349300.

Kobayashi, H., Kiya, H. Bitstream-Based JPEG Image Encryption with File-Size Preserving. IEEE 7 th Global Conference on Consumer Electronics (GCCE), 2018, pp. 1-4. DOI: 10.1109/gcce.2018.8574605.

Li, F., Krivenko, S., Lukin, V. Two-step providing of desired quality in lossy image compression by SPIHT. Radioelektronni i komp'uterni sistemi – Radioelectronic and computer systems, 2020, no. 2, pp. 22-32. DOI: 10.32620/reks.2020.2.02.

Ieremeiev, O., Lukin, V., Okarma, K. Kombinovana metryka vizual'noyi yakosti zobrazhen' dystantsiynoho zonduvannya na osnovi neyronnoyi merezhi [Combined visual quality metric of remote sensing images based on neural network]. Radioelektronni i komp'uterni sistemi – Radioelectronic and computer systems, 2020, no. 4(96), pp. 4-15. DOI: 10.32620/reks.2020.4.01.

Barannik, V. V., Karpinski, M. P., Tverdokhleb, V. V., Barannik, D. V., Himenko, V. V., Aleksander, M. The technology of the video stream intensity controlling based on the bit-planes recombination. 4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS), 2018, pp. 25-28. DOI: 10.1109/IDAACS-SWS.2018.8525560.

Barannik, V., Tupitsya, I., Sidchenko, S., Tarnopolov, R. The method of crypto-semantic presentation of images based on the floating scheme in the basis of the upper boundaries. IEEE Second International Scientific-Practical Conference Problems of Infocommunications Science and Technology (IEEE PIC S&T 2015), 2015, pp. 248-250. DOI: 10.1109/INFOCOMMST.2015.7357326.

Barannik, V., Shulgin, S., Krasnorutsky, A., Slobodyanyuk, O., Gurzhii, P., Korolyova, N. Methodological Fundamentals of Deciphering Coding of Aerophotography Segments on Special Equipment of Unmanned Complex. IEEE 2 nd International Conference on Advanced Trends in Information Theory (IEEE ATIT 2020), 2020, pp. 38-43. DOI: 10.1109/ATIT50783.2020.9349257.

Barannik, V., Ryabukha, Yu., Tverdokhlib, V., Dodukh, A., Suprun, O., Tarasenko, D. Integration the non-equilibrium position encoding into the compression technology of the transformed images. IEEE 14th International Conference on East-West Design & Test Symposium (EWDTS, 2017), 2017, pp. 1-5. DOI: 10.1109/EWDTS.2017.8110030.

Barannik, Vladimir, Barannik, Valeriy, Havrylov, D., Sorokun, A. Development Second and Third Phase of the Selective Frame Processing Method. 3rd International Conference on Advanced Information and Communications Technologies (AICT), 2019, pp. 54-57. DOI: 10.1109/AIACT.2019.8847897.

Barannik, V., Krasnorutskiy, A., Ryabukha, Yu., Okladnoy, D. Model intelligent processing of aerial photographs with a dedicated key features interpretation. IEEE 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 2016, pp. 736-738. DOI: 10.1109/TCSET.2016.7452167.

Barannik, Vladimir., Hahanova, Anna., Krivonos, Vladimir. Coding tangible component of transforms to provide accessibility and integrity of video data. 2013. International Symposium, East-West Design & Test Symposium (EWDTS), 2013, pp. 1-5. DOI: 10.1109/EWDTS.2013.6673179.

Barannik, V. V., Ryabukha, Yu. N., Kulitsa, О. S. The method for improving security of the remote video information resource on the basis of intellectual processing of video frames in the telecommunication systems. Telecommunications and Radio Engineering, 2017, vol. 76, no. 9, pp. 785-797. DOI: 10.1615/TelecomRadEng.v76.i9.40.

Barannik, V. V., Barannik, D., Podlesny, S., Tarasenko, D., Kulitsa, O. The video stream encoding method in infocommunication systems. 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), 2018, pp. 538-541. DOI: 10.1109/TCSET.2018.8336259.

Komolov, D., Zhurbynskyy, D., Kulitsa, O. Selective Method For Hiding Of Video Information Resource In Telecommunication Systems Based On Encryption Of Energy-Significant Blocks Of Reference I-Frame. 1st International Conference on Advanced Information and Communication Technologies (AICT'2015), 2015, pp. 80-83.

Barannik, V., Sidchenko, S., Barannik, N., Khimenko, A. Metod maskuval'noho ushchil'nennya sluzhbovykh danykh v systemakh kompresiyi videozobrazhen' [The method of masking overhead compaction in video compression systems]. Radioelektronni i komp'uterni sistemi – Radioelectronic and computer systems, 2021, no. 2, pp. 51–63. DOI: 10.32620/reks.2021.2.05.