Revisiting Perturbed Quantization

In this work, we revisit Perturbed Quantization steganography with modern tools available to the steganographer today, including near-optimal ternary coding and content-adaptive embedding with side-information. In PQ, side-information in the form of rounding errors is manufactured by recompressing a JPEG image with a judiciously selected quality factor. This side-information, however, cannot be used in the same fashion as in conventional side-informed schemes nowadays as this leads to highly detectable embedding. As a remedy, we utilize the steganographic Fisher information to allocate the payload among DCT modes. In particular, we show that the embedding should not be constrained to contributing coefficients only as in the original PQ but should be expanded to the so-called "contributing DCT modes." This approach is extended to color images by slightly modifying the SI-UNIWARD algorithm. Using the best detectors currently available, it is shown that by manufacturing side information with double compression, one can embed the same amount of information into the doubly-compressed cover image with a significantly better security than applying J-UNIWARD directly in the single-compressed image. At the end of the paper, we show that double compression with the same quality makes side-informed steganography extremely detectable and should be avoided.

[1]  Hui Xiong,et al.  A Comprehensive Survey on Transfer Learning , 2019, Proceedings of the IEEE.

[2]  Jessica J. Fridrich,et al.  Calibration revisited , 2009, MM&Sec '09.

[3]  Patrick Bas,et al.  The ALASKA Steganalysis Challenge: A First Step Towards Steganalysis , 2019, IH&MMSec.

[4]  P. Bas,et al.  ALASKA#2: Challenging Academic Research on Steganalysis with Realistic Images , 2020, 2020 IEEE International Workshop on Information Forensics and Security (WIFS).

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

[6]  J. Davidson,et al.  DOUBLE-COMPRESSED JPEG DETECTION IN A STEGANALYSIS SYSTEM , 2012 .

[7]  Mehdi Boroumand,et al.  Synchronizing Embedding Changes in Side-Informed Steganography , 2020, Media Watermarking, Security, and Forensics.

[8]  Jessica Fridrich,et al.  Effect of JPEG Quality on Steganographic Security , 2019, IH&MMSec.

[9]  Jessica J. Fridrich,et al.  Breaking ALASKA: Color Separation for Steganalysis in JPEG Domain , 2019, IH&MMSec.

[10]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

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

[12]  Rainer Böhme,et al.  Block convergence in repeated transform coding: JPEG-100 forensics, carbon dating, and tamper detection , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

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

[14]  Rémi Cogranne,et al.  Selection-Channel-Aware Reverse JPEG Compatibility for Highly Reliable Steganalysis of JPEG Images , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  J. Fridrich,et al.  ImageNet Pre-trained CNNs for JPEG Steganalysis , 2020, 2020 IEEE International Workshop on Information Forensics and Security (WIFS).

[16]  Jessica J. Fridrich,et al.  How to Pretrain for Steganalysis , 2021, IH&MMSec.

[17]  Guanshuo Xu,et al.  Deep Convolutional Neural Network to Detect J-UNIWARD , 2017, IH&MMSec.

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

[19]  Joan L. Mitchell,et al.  JPEG: Still Image Data Compression Standard , 1992 .

[20]  Jiangqun Ni,et al.  Model-based image steganography using asymmetric embedding scheme , 2018, J. Electronic Imaging.

[21]  Marc Chaumont,et al.  Color images steganalysis using rgb channel geometric transformation measures , 2016, Secur. Commun. Networks.

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

[23]  Zhi-Min He,et al.  Effects of double JPEG compression on steganalysis , 2012, 2012 International Conference on Wavelet Analysis and Pattern Recognition.

[24]  Patrick Bas,et al.  An Empirical Study of Steganography and Steganalysis of Color Images in the JPEG Domain , 2018, IWDW.

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

[26]  Selim Ozcan,et al.  Transfer Learning Effects on Image Steganalysis with Pre-Trained Deep Residual Neural Network Model , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[27]  Yun Q. Shi,et al.  Uniform Embedding for Efficient JPEG Steganography , 2014, IEEE Transactions on Information Forensics and Security.

[28]  Jessica J. Fridrich,et al.  Writing on wet paper , 2005, IEEE Transactions on Signal Processing.

[29]  Marc Chaumont,et al.  Yedroudj-Net: An Efficient CNN for Spatial Steganalysis , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[30]  Patrick Bas,et al.  JPEG Steganography with Side Information from the Processing Pipeline , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[31]  Jiwu Huang,et al.  Distortion function designing for JPEG steganography with uncompressed side-image , 2013, IH&MMSec '13.

[32]  Kaizaburo Chubachi.  An Ensemble Model using CNNs on Different Domains for ALASKA2 Image Steganalysis , 2020, 2020 IEEE International Workshop on Information Forensics and Security (WIFS).

[33]  Marc Chaumont,et al.  Color Image Stegananalysis Using Correlations between RGB Channels , 2015, 2015 10th International Conference on Availability, Reliability and Security.

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

[35]  P. Bas,et al.  Synchronization Minimizing Statistical Detectability for Side-Informed JPEG Steganography , 2020, 2020 IEEE International Workshop on Information Forensics and Security (WIFS).

[36]  Rémi Cogranne,et al.  Steganography by Minimizing Statistical Detectability: The cases of JPEG and Color Images , 2020, IH&MMSec.

[37]  Jessica J. Fridrich,et al.  Perturbed quantization steganography , 2005, Multimedia Systems.

[38]  Jessica Fridrich,et al.  Reverse JPEG Compatibility Attack , 2020, IEEE Transactions on Information Forensics and Security.

[39]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[40]  Jessica Fridrich,et al.  Extending the Reverse JPEG Compatibility Attack to Double Compressed Images , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[41]  Jessica J. Fridrich,et al.  On the role of side information in steganography in empirical covers , 2013, Electronic Imaging.

[42]  Jiwu Huang,et al.  New Channel Selection Rule for JPEG Steganography , 2012, IEEE Transactions on Information Forensics and Security.

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