Highly accurate real-time image steganalysis based on GPU

With the development of steganography, it is required to build high-dimensional feature spaces to detect those sophisticated steganographic schemes. However, the huge time cost prevents the practical deployment of high-dimensional features for steganalysis. SRM and DCTR are important steganalysis feature sets in spatial domain and JPEG domain, respectively. It is necessary to accelerate the extraction of DCTR and SRM to make them more usable in practice, especially for some real-time applications. In this paper, both DCTR and SRM are implemented on the GPU device to exploit the parallel power of the GPU and some optimization methods are presented. For implementation of DCTR, we first utilize the separability and symmetry of two-dimensional discrete cosine transform in decompression and convolution. Then, in order to make phase-aware histograms favorable for parallel GPU processing, we convert them into ordinary 256-dimensional histograms. For SRM, in computing residuals, we specify the computation sequence and spilt the inseparable two-dimensional kernel into several row vectors. When computing the four-dimensional co-occurrences, we convert them into one-dimensional histograms which are more suitable for parallel computing. The experimental results show that the proposed methods can greatly accelerate the extraction of DCTR and SRM, especially for images of large size. Our methods can be applied to the real-time steganalysis system.

[1]  Longkun Guo,et al.  A parallel SRM feature extraction algorithm for steganalysis based on GPU architecture , 2015, Comput. Sci. Inf. Syst..

[2]  Tomás Pevný,et al.  Merging Markov and DCT features for multi-class JPEG steganalysis , 2007, Electronic Imaging.

[3]  Steven W. Smith,et al.  The Scientist and Engineer's Guide to Digital Signal Processing , 1997 .

[4]  Jessica J. Fridrich,et al.  Selection-channel-aware rich model for Steganalysis of digital images , 2014, 2014 IEEE International Workshop on Information Forensics and Security (WIFS).

[5]  Yi Zhang,et al.  Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters , 2015, IH&MMSec.

[6]  Jessica J. Fridrich,et al.  Ensemble Classifiers for Steganalysis of Digital Media , 2012, IEEE Transactions on Information Forensics and Security.

[7]  Jack J. Purdum,et al.  C programming guide , 1983 .

[8]  Jessica J. Fridrich,et al.  Universal distortion function for steganography in an arbitrary domain , 2014, EURASIP Journal on Information Security.

[9]  Yun Q. Shi,et al.  Textural Features for Steganalysis , 2012, Information Hiding.

[10]  Victor Podlozhnyuk,et al.  Image Convolution with CUDA , 2007 .

[11]  Andrew D. Ker.  Implementing the projected spatial rich features on a GPU , 2014, Electronic Imaging.

[12]  Jessica J. Fridrich,et al.  Steganalysis of JPEG images using rich models , 2012, Other Conferences.

[13]  Jessica J. Fridrich,et al.  Steganalysis of Content-Adaptive Steganography in Spatial Domain , 2011, Information Hiding.

[14]  Jessica J. Fridrich,et al.  Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT , 2015, IEEE Transactions on Information Forensics and Security.

[15]  Andreas Kopmann,et al.  UFO: A Scalable GPU-based Image Processing Framework for On-line Monitoring , 2012, 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems.

[16]  Jessica J. Fridrich,et al.  Phase-aware projection model for steganalysis of JPEG images , 2015, Electronic Imaging.

[17]  Qingzhong Liu,et al.  Steganalysis of DCT-embedding based adaptive steganography and YASS , 2011, MM&Sec '11.

[18]  Tomás Pevný,et al.  Modern steganalysis can detect YASS , 2010, Electronic Imaging.

[19]  Tomás Pevný,et al.  Using High-Dimensional Image Models to Perform Highly Undetectable Steganography , 2010, Information Hiding.

[20]  Anton Obukhov,et al.  Discrete Cosine Transform for 8x8 Blocks with CUDA , 2008 .

[21]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[22]  Jessica J. Fridrich,et al.  Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes , 2011, IEEE Transactions on Information Forensics and Security.

[23]  Tomás Pevný,et al.  Steganalysis by Subtractive Pixel Adjacency Matrix , 2009, IEEE Transactions on Information Forensics and Security.

[24]  Jiwu Huang,et al.  Adaptive steganalysis against WOW embedding algorithm , 2014, IH&MMSec '14.

[25]  S. A. Khayam.  The Discrete Cosine Transform ( DCT ) : Theory and Application 1 , 2003 .

[26]  Takeo Kanade,et al.  GPU-accelerated real-time 3D tracking for humanoid locomotion and stair climbing , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  Jessica J. Fridrich,et al.  Random projections of residuals as an alternative to co-occurrences in steganalysis , 2013, Electronic Imaging.