ABSTRACT
Blind image steganalysis is exploring body of digital images for the likely presence of hidden secret messages without knowledge of the employed steganographic technique. This paper proposes a novel image steganalysis technique to attack spatial domain LSBR stego images. The chosen steganalytic feature is the relation between length of the embedded message and the regressed proportion of intensity identical pixels and color channels. A trained SVM analyzes the pixels and the final decision is made based on union of the pixel analysis results. In SW, a number of innovative contributions are made to the field of blind image steganalysis. First, measuring pixel and cannel color correlativity as steganalytic feature. Second, defining pixel membership degree, thereby the pixels gain different level of influence on the process. Third, generating six references for statistical patterns of cover and stego pixels. And fourth, achieving 99.626% steganalyzer sensitivity on 0.25bpp stego images by only two analysis dimensions.
- Karamizadeh, S., et al. 2017. Face Recognition via Taxonomy of Illumination Normalization. Multimedia Forensics and Security. 139--160.Google Scholar
- Shojae Chaeikar, S., Zamani, M., Manaf, A.B.A., Zeki, A.M. 2017. PSW statistical LSB image steganalysis. Multimedia Tools and Applications. Forthcoming 2017.Google Scholar
- Karamizadeh, S., Abdullah, S.M., Shayan, J., Nooralishahi, P., Bagherian, B. 2017. Threshold Based Skin Color Classification. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 9(2-3):131--4.Google Scholar
- Karamizadeh, S., et al. 2015. Face Recognition by Implying Illumination Techniques--A Review Paper. Journal of Science and Engineering. 6(1):001--7.Google Scholar
- Karamizadeh, S., et al. 2017. Taxonomy of Filtering Based Illumination Normalization for Face Recognition. Journal of Telecommunication, Electronic and Computer Engineering (JTEC). 9(1-5):135--9.Google Scholar
- Sharp, T. 2001. An implementation of key-based digital signal steganography. In Proceeding of 4th International Workshop on Information Hiding -- IH 2001. p. 13--26. Google ScholarDigital Library
- Mielikainen, J. 2006. LSB Matching Revisited. IEEE Signal Processing Letters. 13(5):285--287.Google ScholarCross Ref
- Li, W., Zhang, T., Zhu, Z., Zhang, Y., Ping, X. 2013. Detection of LSB Matching Revisited Using Pixel Difference Feature. KSII Transactions on Internet and Information Systems. 7(10): 2514--2526.Google ScholarCross Ref
- Zheng, E., Ping, X., Zhang, T. 2011. Local Linear Transform and New Features of Histogram Characteristic Functions for Steganalysis of Least Significant Bit Matching Steganography. KSII Transactions on Internet and Information Systems. 5(4):840--855.Google ScholarCross Ref
- Fridrich, J., Du, R., Meng, L. 2000. Steganalysis of lsb encoding in colour image. In Proceeding of 2000 IEEE International Conference on Multimedia and Expo. New York (USA), IEEE, c2000.Google Scholar
- Fridrich, J., Goljan, M., Du, R. 2001. Detecting LSB steganography in color and gray-scale images. IEEE Multimedia Magaz. Special Issue on Security:22--28. Google ScholarDigital Library
- Dumitrescu, S., Wu, X., Wang, Z. 2003. Detection of lsb steganography via sample pair analysis. IEEE Trans. Signal Process. 51(7):1995--2007. Google ScholarDigital Library
- Lu, P.Z., et al. 2004. An improved sample pairs method for detection of LSB embedding. In Proceeding of 6th International Workshop on Information Hiding Workshop -- IH 2004. Toronto (Canada), Springer, p. 116--12. Google ScholarDigital Library
- Dumitrescu, S., Wu, X. 2005. A new framework of lsb steganalysis of digital media. IEEE Trans. Signal Process. 53:3936--3947. Google ScholarDigital Library
- Anjum, A., Islam, S. 2016. LSB Steganalysis Using Modified Weighted Stego-Image Method. In Proceeding of International Conference on Signal Processing and Integrated Networks -- SPIN 2016. Noida (India), IEEE, p. 636--641.Google ScholarCross Ref
- Maity, S.P., Maity, S., Sil, J., Delpha, C. 2013. Perceptually adaptive MC-SS image watermarking using GA-NN hybridization in fading gain. Engineering Applications of Artificial Intelligence. 31:3--14.Google ScholarCross Ref
- Mohammadi, F. G., Abadeh, M.S. 2014. Image steganalysis using a bee colony based feature selection algorithm. Engineering Applications of Artificial Intelligence. 31:35--43.Google ScholarCross Ref
- Guan, Q., Dong, J., Tan, T. 2011. An effective image steganalysis method based on neighborhood information of pixels. In Proceeding of 18th IEEE International Conference on Image Processing. Brussels, Belgium, IEEE.Google ScholarCross Ref
- Lerch-Hostalot, D., Megias, D. 2013. LSB matching steganalysis based on patterns of pixel differences and random embedding. Computers & security. 32:192--206.Google Scholar
- Lerch-Hostalot, D., Megías, D. 2016. Unsupervised steganalysis based on artificial training sets. Engineering Applications of Artificial Intelligence. 50:45--59. Google ScholarDigital Library
- Alizadeh, M., et al. 2013. A brief review of mobile cloud computing opportunities. Research Notes in Information Science. 12:155--60.Google Scholar
- Chaeikar, S. S., Manaf, A. B. A., & Zamani, M. 2012. Comparative analysis of Master-key and Interpretative Key Management (IKM) frameworks. In Cryptography and security in computing. InTech.Google Scholar
- Taherdoost, H., et al. 2012. Smart card adoption model: Social and ethical perspectives. Science. 3(4).Google Scholar
- Yazdanpanah, S., et al. 2011. Security features comparison of master key and IKM cryptographic key management for researchers and developers. In Proceeding of International Conference on Software Technology and Engineering, 3rd (ICSTE 2011). ASME Press.Google ScholarCross Ref
- Chaeikar, S. S., Zamani, M., Chukwuekezie, C. S., & Alizadeh, M. 2013. Electronic Voting Systems for European Union Countries. Journal of Next Generation Information Technology, 4(5), 16.Google ScholarCross Ref
- Mazdak, Z., Azizah, B. A. M., Shahidan, M. A., & Saman, S. C. (2012). Mazdak technique for PSNR estimation in audio steganography. In Applied Mechanics and Materials (Vol. 229, pp. 2798--2803). Trans Tech Publications.Google ScholarCross Ref
Index Terms
- SW: a blind LSBR image steganalysis technique
Recommendations
PSW statistical LSB image steganalysis
Steganography is the art and science of producing covert communications by concealing secret messages in apparently innocent media, while steganalysis is the art and science of detecting the existence of these. This manuscript proposes a novel blind ...
High payload steganography mechanism using hybrid edge detector
Steganography is the art and science of hiding data into information. The secret message is hidden in such a way that no one can apart from the sender or the intended recipient. The least significant bit (LSB) substitution mechanism is the most common ...
Classification of steganalysis techniques: A study
Steganography is the art of secret communication and steganalysis is the art of detecting the hidden messages embedded in digital media using steganography. Both steganography and steganalysis have received a great deal of attention from law enforcement ...
Comments