Xiaofeng Song
ABSTRACT
Adaptive JPEG steganographic schemes are difficult to preserve the image texture features in all scales and orientations when the embedding changes are constrained to the complicated texture regions, then a steganalysis feature extraction method is proposed based on 2 dimensional (2D) Gabor filters. The 2D Gabor filters have certain optimal joint localization properties in the spatial domain and in the spatial frequency domain. They can describe the image texture features from different scales and orientations, therefore the changes of image statistical characteristics caused by steganography embedding can be captured more effectively. For the proposed feature extraction method, the decompressed JPEG image is filtered by 2D Gabor filters with different scales and orientations firstly. Then, the histogram features are extracted from all the filtered images.Lastly, the ensemble classifier is used to assemble the proposed steganalysis feature as well as the final steganalyzer. The experimental results show that the proposed steganalysis feature can achieve a competitive performance by comparing with the other steganalysis features when they are used for the detection performance of adaptive JPEG steganography such as UED, JUNIWARD and SI-UNIWARD.
- P. Bas, T. Filler, and T. Pevny. "break our steganographic system": The ins and outs of organizing boss. In Information Hiding, pages 59--70. Springer, 2011. Google Scholar
Digital Library
- J. G. Daugman. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. JOSA A, 2(7):1160--1169, 1985.Google Scholar
- T. Denemark, V. Sedighi, V. Holub, R. Cogranne, and J. Fridrich. Selection-channel-aware rich model for steganalysis of digital images. In IEEE Workshop on Information Forensic and Security, Atlanta, GA, 2014.Google ScholarCross Ref
- T. Filler and J. Fridrich. Design of adaptive steganographic schemes for digital images. In IS&T/SPIE Electronic Imaging, pages 78800F--78800F. International Society for Optics and Photonics, 2011.Google Scholar
- T. Filler, J. Judas, and J. Fridrich. Minimizing additive distortion in steganography using syndrome-trellis codes. Information Forensics and Security, IEEE Transactions on, 6(3):920--935, 2011. Google ScholarDigital Library
- J. Fridrich, M. Goljan, P. Lisonek, and D. Soukal. Writing on wet paper. Signal Processing, IEEE Transactions on, 53(10):3923--3935, 2005. Google ScholarDigital Library
- J. Fridrich, M. Goljan, and D. Soukal. Perturbed quantization steganography. Multimedia Systems, 11(2):98--107, 2005. Google ScholarDigital Library
- J. Fridrich and J. Kodovsky. Rich models for steganalysis of digital images. Information Forensics and Security, IEEE Transactions on, 7(3):868--882, 2012. Google ScholarDigital Library
- J. Fridrich, T. Pevny, and J. Kodovsky. Statistically undetectable jpeg steganography: dead ends challenges, and opportunities. In Proceedings of the 9th workshop on Multimedia & security, pages 3--14. ACM, 2007. Google ScholarDigital Library
- S. E. Grigorescu, N. Petkov, and P. Kruizinga. Comparison of texture features based on gabor filters. Image Processing, IEEE Transactions on, 11(10):1160--1167, 2002. Google ScholarDigital Library
- L. Guo, J. Ni, and Y.-Q. Shi. An efficient jpeg steganographic scheme using uniform embedding. In WIFS, pages 169--174, 2012.Google ScholarCross Ref
- V. Holub and J. Fridrich. Digital image steganography using universal distortion. In Proceedings of the first ACM workshop on Information hiding and multimedia security, pages 59--68. ACM, 2013. Google ScholarDigital Library
- V. Holub and J. Fridrich. Random projections of residuals for digital image steganalysis. Information Forensics and Security, IEEE Transactions on, 8(12):1996--2006, 2013. Google ScholarDigital Library
- V. Holub and J. Fridrich. Low complexity features for jpeg steganalysis using undecimated dct. Information Forensics and Security, IEEE Transactions on, 10(2):219--228, 2015.Google Scholar
- V. Holub and J. Fridrich. Phase-aware projection model for steganalysis of jpeg images. Proc. SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics XVII, to appear, San Francisco, CA, 2015.Google Scholar
- Y. Kim, Z. Duric, and D. Richards. Modified matrix encoding technique for minimal distortion steganography. In Information hiding, pages 314--327. Springer, 2007. Google ScholarDigital Library
- J. Kodovsky and J. Fridrich. Steganalysis of jpeg images using rich models. In IS&T/SPIE Electronic Imaging, pages 83030A--83030A. International Society for Optics and Photonics, 2012.Google Scholar
- J. Kodovsky, J. Fridrich, and V. Holub. On dangers of overtraining steganography to incomplete cover model. In Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security, pages 69--76. ACM, 2011. Google ScholarDigital Library
- J. Kodovsky, J. Fridrich, and V. Holub. Ensemble classifiers for steganalysis of digital media. Information Forensics and Security, IEEE Transactions on, 7(2):432--444, 2012. Google ScholarDigital Library
- T. Pevny and J. Fridrich. Merging markov and dct features for multi-class jpeg steganalysis. In Electronic Imaging 2007, pages 650503--650503. International Society for Optics and Photonics, 2007.Google ScholarCross Ref
- N. Provos. Defending against statistical steganalysis. In Usenix Security Symposium, volume 10, pages 323--336, 2001. Google ScholarDigital Library
- P. Sallee. Model-based steganography. In Digital watermarking, pages 154--167. Springer, 2004.Google ScholarCross Ref
- X. Song, F. Liu, X. Luo, J. Lu, and Y. Zhang. Steganalysis of perturbed quantization steganography based on the enhanced histogram features. Multimedia Tools and Applications, pages 1--27, 2014.Google Scholar
- C. Wang and J. Ni. An efficient jpeg steganographic scheme based on the block entropy of dct coefficients. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, pages 1785--1788. IEEE, 2012.Google ScholarCross Ref
- A. Westfeld. F5-a steganographic algorithm. In Information hiding, pages 289--302. Springer, 2001. Google ScholarDigital Library
Index Terms
- Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters
Recommendations
2D Gabor filters-based steganalysis of content-adaptive JPEG steganography
To improve the detection accuracy for content-adaptive JPEG steganography, which often constrains the embedding changes to complex texture regions, a new steganalysis feature called the GRF (Gabor Rich Feature) based on two-dimensional (2D) Gabor ...
Steganalysis of Content-Adaptive JPEG Steganography Based on CNN and 2D Gabor Filters
CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial IntelligenceAiming at the problem of choosing high-pass filters in image processing layer of deep CNN (Convolution Neural Network) for steganalysis, this paper studies the effect of different filter banks on detection performance for content-adaptive JPEG ...
JPEG Steganography Using BPCS
SIGITE '19: Proceedings of the 20th Annual SIG Conference on Information Technology EducationSteganography is the art of camouflage: its purpose is to make a message go unnoticed within another message. It differs from cryptography, the "art of secrecy", which seeks to make a message unintelligible to anyone other than the lawful. The concept ...
Comments