Practical Cover Selection for Steganography

This letter focus on a practical scenario of cover selection for steganography, in which a part of the available images of the sender have been processed to improve visual quality, e.g., contrast enhancement, image denoising. In this case, not only the embedding distortion (caused by steganography), but also the processing distortion (caused by processing) should be considered when selecting cover image. We propose a cover selection method to combine the two kinds of distortion together to measure the suitability for steganography of the available images. To calculate the processing distortion, a classifier is trained to distinguish the processed images with the original ones. The classifier is then used to measure the possibility of the existence of processing. High possibility means high processing distortion. In addition, the current distortion minimization framework designed for steganography is employed to calculate the embedding distortion. Finally, both kinds of distortion are combined to form the total distortion, and the image with the minimal total distortion is selected as cover. Using the selected image for embedding, high undetectability can be achieved.

[1]  Roman V. Meshcheryakov,et al.  Approach to the selection of the best cover image for information embedding in JPEG images based on the principles of the optimality , 2018, J. Decis. Syst..

[2]  Jessica J. Fridrich,et al.  Practical methods for minimizing embedding impact in steganography , 2007, Electronic Imaging.

[3]  Xinpeng Zhang,et al.  Hybrid distortion function for JPEG steganography , 2016, J. Electronic Imaging.

[4]  Zhenxing Qian,et al.  On Improving Distortion Functions for JPEG Steganography , 2018, IEEE Access.

[5]  Vijay H. Mankar,et al.  Curvelet transform and cover selection for secure steganography , 2018, Multimedia Tools and Applications.

[6]  Tomás Pevný,et al.  Steganalysis by subtractive pixel adjacency matrix , 2010, IEEE Trans. Inf. Forensics Secur..

[7]  Xinpeng Zhang,et al.  Toward Construction-Based Data Hiding: From Secrets to Fingerprint Images , 2019, IEEE Transactions on Image Processing.

[8]  Zhenxing Qian,et al.  An Improved Steganalysis Method Using Feature Combinations , 2019, ICAIS.

[9]  Jiwu Huang,et al.  Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework , 2016, IEEE Transactions on Information Forensics and Security.

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

[11]  Xinpeng Zhang,et al.  Joint Cover-Selection and Payload-Allocation by Steganographic Distortion Optimization , 2018, IEEE Signal Processing Letters.

[12]  Tomás Pevný,et al.  "Break Our Steganographic System": The Ins and Outs of Organizing BOSS , 2011, Information Hiding.

[13]  Hedieh Sajedi,et al.  Cover Selection Steganography Method Based on Similarity of Image Blocks , 2008, 2008 IEEE 8th International Conference on Computer and Information Technology Workshops.

[14]  Zhenxing Qian,et al.  New Framework of Reversible Data Hiding in Encrypted JPEG Bitstreams , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

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

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

[17]  Jiangqun Ni,et al.  Deep Learning Hierarchical Representations for Image Steganalysis , 2017, IEEE Transactions on Information Forensics and Security.

[18]  Hedieh Sajedi,et al.  Using contourlet transform and cover selection for secure steganography , 2010, International Journal of Information Security.

[19]  Jianhua Yang,et al.  An Embedding Cost Learning Framework Using GAN , 2020, IEEE Transactions on Information Forensics and Security.

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

[21]  Mo Chen,et al.  Deep Residual Network for Steganalysis of Digital Images , 2019, IEEE Transactions on Information Forensics and Security.

[22]  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).

[23]  Xinpeng Zhang,et al.  Distortion function based on residual blocks for JPEG steganography , 2017, Multimedia Tools and Applications.

[24]  Xinpeng Zhang,et al.  Secure Cover Selection for Steganography , 2019, IEEE Access.

[25]  Gerald Schaefer,et al.  UCID: an uncompressed color image database , 2003, IS&T/SPIE Electronic Imaging.

[26]  Chuan Qin,et al.  Multiple Robustness Enhancements for Image Adaptive Steganography in Lossy Channels , 2020, IEEE Transactions on Circuits and Systems for Video Technology.

[27]  Yan Liu,et al.  What makes the stego image undetectable? , 2015, ICIMCS '15.

[28]  Nasir D. Memon,et al.  Cover Selection for Steganographic Embedding , 2006, 2006 International Conference on Image Processing.

[29]  Jessica J. Fridrich,et al.  Designing steganographic distortion using directional filters , 2012, 2012 IEEE International Workshop on Information Forensics and Security (WIFS).

[30]  Xinpeng Zhang,et al.  Towards Robust Image Steganography , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[31]  Bin Li,et al.  A new cost function for spatial image steganography , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[32]  Ximeng Liu,et al.  Person Re-Identification over Encrypted Outsourced Surveillance Videos , 2019, IEEE Transactions on Dependable and Secure Computing.