Robust Adaptive Steganography Based on Dither Modulation and Modification With Re-Compression

Traditional adaptive steganography is a technique used for covert communication with high security, but it is invalid in the case of stego images are sent to legal receivers over networks which is lossy, such as JPEG compression of channels. To deal with such problem, robust adaptive steganography is proposed to enable the receiver to extract secret messages from the damaged stego images. Previous works utilize reverse engineering and compression-resistant domain constructing to implement robust adaptive steganography. In this paper, we adopt modification with re-compression scheme to improve the robustness of stego sequences in stego images. To balance security and robustness, we move the embedding domain to the low frequency region of DCT (Discrete Cosine Transform) coefficients to improve the security of robust adaptive steganography. In addition, we add additional check codes to further reduce the average extraction error rate based on the framework of E-DMAS (Enhancing Dither Modulation based robust Adaptive Steganography). Compared with GMAS (Generalized dither Modulation based robust Adaptive Steganography) and E-DMAS, experiment results show that our scheme can achieve strong robustness and improve the security of robust adaptive steganography greatly when the channel quality factor is known.

[1]  Kejiang Chen,et al.  Robust adaptive steganography based on generalized dither modulation and expanded embedding domain , 2020, Signal Process..

[2]  Weiming Zhang,et al.  On the fault-tolerant performance for a class of robust image steganography , 2018, Signal Process..

[3]  Pramod K. Varshney,et al.  Data Falsification Attacks on Consensus-Based Detection Systems , 2017, IEEE Transactions on Signal and Information Processing over Networks.

[4]  Philip Koopman,et al.  Cyclic redundancy code (CRC) polynomial selection for embedded networks , 2004, International Conference on Dependable Systems and Networks, 2004.

[5]  Chuan Qin,et al.  Dither modulation based adaptive steganography resisting jpeg compression and statistic detection , 2017, Multimedia Tools and Applications.

[6]  Hong Zhang,et al.  Improving the Robustness of Adaptive Steganographic Algorithms Based on Transport Channel Matching , 2019, IEEE Transactions on Information Forensics and Security.

[7]  Nenghai Yu,et al.  Motion vector modification distortion analysis-based payload allocation for video steganography , 2021, J. Vis. Commun. Image Represent..

[8]  Fenlin Liu,et al.  A JPEG-Compression Resistant Adaptive Steganography Based on Relative Relationship between DCT Coefficients , 2015, 2015 10th International Conference on Availability, Reliability and Security.

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

[10]  Hang Zhou,et al.  Reversible Data Hiding in Encrypted Three-Dimensional Mesh Models , 2018, IEEE Transactions on Multimedia.

[11]  Weiwei Liu,et al.  Damage-resistance matrix embedding framework: the contradiction between robustness and embedding efficiency , 2015, Secur. Commun. Networks.

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

[13]  Hideki Noda,et al.  High-performance JPEG steganography using quantization index modulation in DCT domain , 2006, Pattern Recognit. Lett..

[14]  Heng Zhang,et al.  A Novel Data Fusion Algorithm to Combat False Data Injection Attacks in Networked Radar Systems , 2018, IEEE Transactions on Signal and Information Processing over Networks.

[15]  Tong Qiao,et al.  Robust Steganography by Modifying Sign of DCT Coefficients , 2019, IEEE Access.

[16]  G. David Forney,et al.  On decoding BCH codes , 1965, IEEE Trans. Inf. Theory.

[17]  Adrian G. Bors,et al.  Enhancing reliability and efficiency for real-time robust adaptive steganography using cyclic redundancy check codes , 2020, Journal of Real-Time Image Processing.

[18]  Gregory W. Wornell,et al.  Quantization index modulation: A class of provably good methods for digital watermarking and information embedding , 2001, IEEE Trans. Inf. Theory.

[19]  Fenlin Liu,et al.  A framework of adaptive steganography resisting JPEG compression and detection , 2016, Secur. Commun. Networks.

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

[21]  Bolin Chen,et al.  Audio Steganography Based on Iterative Adversarial Attacks Against Convolutional Neural Networks , 2020, IEEE Transactions on Information Forensics and Security.

[22]  Fei Peng,et al.  Reversible Data Hiding in Encrypted 2D Vector Graphics Based on Reversible Mapping Model for Real Numbers , 2019, IEEE Transactions on Information Forensics and Security.

[23]  Huaiqing Wang,et al.  Cyber warfare: steganography vs. steganalysis , 2004, CACM.

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

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

[26]  Yun Q. Shi,et al.  Using Statistical Image Model for JPEG Steganography: Uniform Embedding Revisited , 2015, IEEE Transactions on Information Forensics and Security.

[27]  O. Antoine,et al.  Theory of Error-correcting Codes , 2022 .

[28]  Fei Peng,et al.  Separable Robust Reversible Watermarking in Encrypted 2D Vector Graphics , 2020, IEEE Transactions on Circuits and Systems for Video Technology.

[29]  Fenlin Liu,et al.  Joint JPEG compression and detection resistant performance enhancement for adaptive steganography using feature regions selection , 2017, Multimedia Tools and Applications.

[30]  Andreas Spanias,et al.  Analysis and Design of Robust Max Consensus for Wireless Sensor Networks , 2019, IEEE Transactions on Signal and Information Processing over Networks.

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

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

[33]  Zhibin Pan,et al.  Reversible data hiding method based on combining IPVO with bias-added Rhombus predictor by multi-predictor mechanism , 2021, Signal Process..

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