Side Attacks on Stegosystems Executing Message Encryption Previous to Embedding

There are introduced two new steganalytic methods not depending on the statistics of the cover objects, namely side attacks stegosystems. The first one assumes that the plaintext, encrypted before embedding, is partly known by the attacker. In this case, the stegosystems detection is based on the calculation of mutual information between message and extracted encrypted data. For this calculation, a notion of the k-nearest neighbor distance is applied. The second method is applied to HUGO, one of the most efficient steganographic algorithms. In this case the stegosystems detection is based on a verification of the NIST tests to the extracted encrypted messages. Moreover, we show that the problem to find a submatrix of the embedding matrix determining a trellis code structure in the HUGO algorithm provides a search of the stegokey by the proposed method.

[1]  Mamta Jain.  A Review on Data Leakage Prevention using Image Steganography , 2016 .

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

[3]  Valery Korzhik,et al.  Steganographic applications of the nearest-neighbor approach to Kullback-Leibler divergence estimation , 2015, 2015 Third International Conference on Digital Information, Networking, and Wireless Communications (DINWC).

[4]  Elaine B. Barker,et al.  A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications , 2000 .

[5]  Tao Han,et al.  Stego key searching for LSB steganography on JPEG decompressed image , 2015, Science China Information Sciences.

[6]  Zenon Chaczko,et al.  Hyper Edge Detection with Clustering for Data Hiding , 2016, J. Inf. Hiding Multim. Signal Process..

[7]  Jessica J. Fridrich,et al.  Model Based Steganography with Precover , 2017, Media Watermarking, Security, and Forensics.

[8]  Jessica J. Fridrich,et al.  Improving Steganographic Security by Synchronizing the Selection Channel , 2015, IH&MMSec.

[9]  Guillermo Morales-Luna,et al.  Steganalysis Based on Statistical Properties of the Encrypted Messages , 2017, MMM-ACNS.

[10]  Jessica Fridrich,et al.  Steganography in Digital Media: References , 2009 .

[11]  Jing Liu,et al.  Stego Key Estimation in LSB Steganography , 2012, J. Multim..

[12]  Sanjeev R. Kulkarni,et al.  A Nearest-Neighbor Approach to Estimating Divergence between Continuous Random Vectors , 2006, 2006 IEEE International Symposium on Information Theory.

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

[14]  Guillermo Morales-Luna,et al.  Using the generalised Viterbi algorithm to achieve a highly effective stegosystem for images , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[15]  Claude E. Shannon,et al.  Communication theory of secrecy systems , 1949, Bell Syst. Tech. J..

[16]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  Sushil Jajodia,et al.  Steganalysis: the investigation of hidden information , 1998, 1998 IEEE Information Technology Conference, Information Environment for the Future (Cat. No.98EX228).

[18]  Scott R. Ellis.  Fundamentals of Cryptography , 2014 .

[19]  Guangjie Liu,et al.  Alternative Syndrome-Trellis Codes With Reduced Trellis Complexity , 2014, J. Inf. Hiding Multim. Signal Process..

[20]  Fenlin Liu,et al.  Reliable steganalysis of HUGO steganography based on partially known plaintext , 2017, Multimedia Tools and Applications.

[21]  Howard M. Heys,et al.  A TUTORIAL ON LINEAR AND DIFFERENTIAL CRYPTANALYSIS , 2002, Cryptologia.

[22]  Christian Cachin,et al.  An information-theoretic model for steganography , 1998, Inf. Comput..

[23]  Tomás Pevný,et al.  Towards Multi-class Blind Steganalyzer for JPEG Images , 2005, IWDW.