A Natural Steganography Embedding Scheme Dedicated to Color Sensors in the JPEG Domain

Using Natural Steganography (NS), a cover raw image acquired at sensitivity ISO 1 is transformed into a stego image whose statistical distribution is similar to a cover image acquired at sensitivity ISO 2 > ISO 1. This paper proposes such an embedding scheme for color sensors in the JPEG domain, extending thus the prior art proposed for the pixel domain and the JPEG domain for monochrome sensors. We first show that color sensors generate strong intra-block and inter-block dependencies between DCT coefficients and that theses dependencies are due to the demosaicking step in the development process. Capturing theses dependencies using an empirical covariance matrix, we propose a pseudo-embedding algorithm on greyscale JPEG images which uses up to four sub-lattices and 64 lattices to embed information while preserving the estimated correlations among DCT coefficients. We then compute an approximation of the average embedding rate w.r.t. the JPEG quality factor and evaluate the empirical security of the proposed scheme for linear and non-linear demosaicing schemes. Our experiments show that we can achieve high capacity (around 2 bit per nzAC) with a high empirical security (P E 30% using DCTR at QF 95).

[1]  Kejiang Chen,et al.  Defining Joint Distortion for JPEG Steganography , 2018, IH&MMSec.

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

[3]  Jessica J. Fridrich,et al.  Side-informed steganography with additive distortion , 2015, 2015 IEEE International Workshop on Information Forensics and Security (WIFS).

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

[5]  Jessica J. Fridrich,et al.  Content-Adaptive Steganography by Minimizing Statistical Detectability , 2016, IEEE Transactions on Information Forensics and Security.

[6]  Jessica J. Fridrich,et al.  Natural Steganography in JPEG Compressed Images , 2018, Media Watermarking, Security, and Forensics.

[7]  Tomás Pevný,et al.  Is ensemble classifier needed for steganalysis in high-dimensional feature spaces? , 2015, 2015 IEEE International Workshop on Information Forensics and Security (WIFS).

[8]  Phil Sallee,et al.  Model-Based Steganography , 2003, IWDW.

[9]  Patrick Bas,et al.  An embedding mechanism for natural steganography after down-sampling , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[11]  D. Rajan Probability, Random Variables, and Stochastic Processes , 2017 .

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

[13]  Patrick Bas,et al.  Steganography via cover-source switching , 2016, 2016 IEEE International Workshop on Information Forensics and Security (WIFS).