UNIVERSAL APPROACH TO THE IDENTIFICATION OF STEGANOGRAPHIC TRANSFORMATION OF THE SPATIAL DOMAIN OF DIGITAL IMAGES
Keywords:
Steganalysis, Digital Image, Spatial DomainAbstract
The article proposes a universal approach to detect the presence of additional information attachments in the spatial domain of digital images. The approach is based on the use of the steganalytic method developed by the author earlier and based on the analysis of sequential triads of triplets in the matrix of unique colors of image. The steganoanalytic method Color Triads allows to detect with high accuracy the additional information attachments embedded by various steganographic methods into the spatial domain of images. The perturbations in the matrix of unique colors of images as a result of steganographic transformation are illustrated which concludes about the sensitivity of the blue color component even to small modifications of the brightness values of this matrix. The efficiency of detecting stegoes formed at small values of payload (0.4 bpp and less) based on LSB, S-UNIWARD, MiPOD and WOW steganographic methods is shown. The obtained results of computational experiments allow detect the filled color components of digital images with high accuracy even at payload of 0.1 and 0.05 bpp that is much higher than the results of modern analogues. The steganalytic method analyzes the spatial domain of images, which avoids the accumulation of computational errors that affect the detection result.
References
Bohme, R.: Advanced statistical steganalysis. Springer, Berlin (2010).
Wu, D.C., Tsai, W.H.: A Steganographic method for images by pixel-value differencing. Pattern Recognition Letters, vol.24, 1613-1626 (2003).
Filler, T., Fridrich, J.: Gibbs Construction in Steganography. IEEE Transactions on Information Forensics and Security, vol.4, iss.5, 705-720 (2010).
Holub, V., Fridrich, J.: Designing Steganographic Distortion Using Directional Filters. In: 2012 IEEE International Workshop on Information Forensics and Security (WIFS 2012), pp. 1-6. IEEE, Tenerife, Canary Islands (2012).
Fridrich, J., Kodovsky, J.: Multivariate Gaussian model for designing additive distortion for steganography. In Proc. IEEE, International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), pp. 1-5. IEEE, Vancouver, Canada (2013).
Holub, V., Fridrich, J., Denemark, T.: Universal Distortion Function for Steganography in an Arbitrary Domain. EURASIP Journal on Information Security (Section: SI: Revised Selected Papers of ACM IH and MMS 2013), 1-13 (2014).
Shuliang Sun, Yongning Guo: A Novel Image Steganography Based on Contourlet Transform and Hill Cipher. Journal of Information Hiding and Multimedia Signal Processing, vol.6, no.5, 889-897 (2015).
Sedighi, V., Cogranne, R., Fridrich, J.: Content-Adaptive Steganography by Minimizing Statistical Detectability. IEEE Transactions on Information Forensics and Security, vol.11, iss.2, 221-234 (2016).
Lerch-Hostalot, D. Meg´ıas, D.: Unsupervised steganalysis based on artificial training sets. Engineering Applications of Artificial Intelligence, vol.50, 45-59 (2016).
Denemark, T., Fridrich, J., Comesaña, P.: Improving Selection-Channel-Aware Steganalysis Features. Media Watermarking, Security, and Forensics, 1-8 (2016).
Jian Ye, Jiangqun Ni, Yang Yi: Deep Learning Hierarchical Representations for Image Steganalysis. IEEE Transactions on Information Forensics and Security, vol.12, iss.11, 2545-2557 (2017).
Couchot, J.-F., Couturier, R., Salomon, M.: Improving Blind Steganalysis in Spatial Domain Using a Criterion to Choose the Appropriate Steganalyzer Between CNN and SRM+EC. In IFIP International Conference on ICT Systems Security and Privacy Protection, pp. 327-340. Springer, Rome, Italy (2017).
Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. Information Forensics and Security, IEEE Transactions, vol.7, no.2, 432-444 (2012).
Fong, D.C.-L., Saunders, M.: LSMR: An iterative algorithm for sparse least-squares problems. SIAM Journal on Scientific Computing, vol. 33, no. 5, 2950-2971 (2011).
Cogranne, R., Sedighi, V., Fridrich, J., Pevný, T.: Is Ensemble Classifier Needed for Steganalysis in High-Dimensional Feature Spaces? IEEE International Workshop on Information Forensics and Security (WIFS 2015), pp.1-6. IEEE, Rome, Italy (2015).
Salomon, M., Couturier, R., Guyeux, C., Couchot, J.-F., Bahi, J.M.: Steganalysis via a convolutional neural network using large convolution filters for embedding process with same stego key: A deep learning approach for telemedicine. European Research in Telemedicine / La Recherche Européenne en Télémédecine 6, 79-92 (2017).
Kobozeva, A.A., Bobok, I.I., Garbuz, A.I.: General Principles of Integrity Checking of Digital Images and Application for Steganalysis. Transport and Telecommunication, vol.17, no.2, 128-137 (2016).
Bobok, I.I.: Application of ROC-analysis for integrated assessment of steganalysis method’s efficiency. Informatics and Mathematical Methods in Simulation, vol. 2, no. 3, 221-230 (2012).
Akhmametieva, A.: Steganalysis of digital contents, based on the analysis of unique color triplets. Annales Mathematicae et Informaticae, no. 47, 3-18 (2017).
NRCS Photo Gallery, http://photogallery.nrcs.usda.gov, last accessed 2016/03/14.
Ker, A.D.: Steganalysis of LSB matching in grayscale images. IEEE Signal Processing Letters, vol. 12, no. 6, 441-444 (2005).
Liu, Q.Z., Sung A.H.: Image complexity and feature mining for steganalysis of least significant bit matching steganography. Information Sciences, vol. 178, no. 1, 21-36 (2008).
Zhihua Xia, Lincong Yang: A Learning-Based Steganalytic Method against LSB Matching Steganography. Radioengineering, vol. 20, no. 1, 102-109 (2011).