Robust steganographic method based on unconventional approach of neural networks

Abstract The article deals with the issue of using an apparatus of neural networks in the area of steganography. A new method called STEGONN was proposed. The proposed method is robust enough to an attack and the hidden message hard to be falsified. The core of our work lies in a design and implementation of a method for the use of neural networks as a native coder and decoder of a secret message (digital watermark) with an emphasis on the minimum necessary level of image data modification – covermedium. A covermedium is not perceived as a passive cover of a secret message, but we make active use of cover medium data, primarily its data markers (image markers) to insert a secret message. The advantage over other steganographic methods is the fact that the method implicitly offer the possibility to detect corrupted parts of the stegomedium and inform the user about possible manipulation with the image. The characteristics of the proposed method have been experimentally verified and compared with commercially available steganographic applications.

[1]  Chengyun Yang,et al.  Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[2]  Xindong Wu,et al.  On digital image trustworthiness , 2016, Appl. Soft Comput..

[3]  Shaohui Liu,et al.  Steganalysis based on wavelet texture analysis and neural network , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[4]  Hassan Mathkour,et al.  A Novel Approach for Hiding Messages in Images , 2009, 2009 International Conference on Signal Acquisition and Processing.

[5]  Po-Yueh Chen,et al.  A DWT Based Approach for Image Steganography , 2006 .

[6]  Shadrokh Samavi,et al.  Steganalysis and payload estimation of embedding in pixel differences using neural networks , 2010, Pattern Recognit..

[7]  A. Taheri,et al.  A new robust digital image watermarking technique based on the Discrete Cosine Transform and Neural Network , 2008, 2008 International Symposium on Biometrics and Security Technologies.

[8]  Roman Senkerik,et al.  Comparison between Neural Network Steganalysis and Linear Classification Method Stegdetect , 2010, 2010 Second International Conference on Computational Intelligence, Modelling and Simulation.

[9]  Kevin Curran,et al.  Digital image steganography: Survey and analysis of current methods , 2010, Signal Process..

[10]  J. Mielikainen.  LSB matching revisited , 2006, IEEE Signal Processing Letters.

[11]  Niels Provos,et al.  Hide and Seek: An Introduction to Steganography , 2003, IEEE Secur. Priv..

[12]  Amir-Masoud Eftekhari-Moghadam,et al.  An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method , 2015, Appl. Soft Comput..

[13]  Alexander A. Sirota,et al.  Neural network functional models and algorithms for information conversion in order to create digital watermarks , 2015 .

[14]  Haitham Badi,et al.  Artificial neural network for steganography , 2014, Neural Computing and Applications.

[15]  Yao Hongxun,et al.  Neural network based steganalysis in still images , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[16]  B. Geetha,et al.  High Secure Image Steganography based on Hopfield Chaotic Neural Network and Wavelet Transforms , 2013 .

[17]  Chuan-Yu Chang,et al.  Using Counter-propagation Neural Network for Robust Digital Audio Watermarking in DWT Domain , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.