Extreme learning machine based optimal embedding location finder for image steganography

In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.

[1]  S. Selvakumar,et al.  Blind Image Steganalysis of JPEG images using feature extraction through the process of dilation , 2014, Digit. Investig..

[2]  Gautam Sanyal,et al.  Hiding & Analyzing Data in Image Using Extended PMM☆ , 2013 .

[3]  El-Sayed M. El-Alfy.  Detecting pixel-value differencing steganography using Levenberg-Marquardt neural network , 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[4]  De-Shuang Huang,et al.  Intelligent Computing Theory , 2014, Lecture Notes in Computer Science.

[5]  Putra Sumari,et al.  Steganography in digital images: Common approaches and tools , 2013 .

[6]  R. Bhavani,et al.  A modified secure digital image steganography based on Discrete Wavelet Transform , 2012, 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET).

[7]  Hamidreza Rashidy Kanan,et al.  A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm , 2014, Expert Syst. Appl..

[8]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[9]  C.-C. Jay Kuo,et al.  Block-based image steganalysis: Algorithm and performance evaluation , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[10]  Hossein Najafi.  A neural network approach to digital data hiding based on the perceptual masking model of the human vision system , 2010, Int. J. Intell. Comput. Cybern..

[11]  S. M. Elshoura,et al.  A secure high capacity full-gray-scale-level multi-image information hiding and secret image authentication scheme via Tchebichef moments , 2013, Signal Process. Image Commun..

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

[13]  Jiwu Huang,et al.  Edge Adaptive Image Steganography Based on LSB Matching Revisited , 2010, IEEE Transactions on Information Forensics and Security.

[14]  Yizhang Jiang,et al.  Feedforward kernel neural networks, generalized least learning machine, and its deep learning with application to image classification , 2015, Appl. Soft Comput..

[15]  Mohammad Rahmati,et al.  An efficient steganographic framework based on dynamic blocking and genetic algorithm , 2014, Multimedia Tools and Applications.

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

[17]  Adel Al-Mohammad,et al.  Steganography-Based Secret and Reliable Communications: Improving Steganographic Capacity and Imperceptibility , 2010 .

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

[19]  Roman Senkerik,et al.  Steganography Detection by Means of Neural Networks , 2008, 2008 19th International Workshop on Database and Expert Systems Applications.

[20]  Yen-Ping Chu,et al.  A high capacity reversible data hiding scheme with edge prediction and difference expansion , 2009, J. Syst. Softw..

[21]  Zuzana Kominkova Oplatkova,et al.  Steganography content detection by means of feedforward neural network , 2013 .

[22]  Zhaohong Deng,et al.  T 2 FELA : Type-2 Fuzzy Extreme Learning Algorithm for Fast Training of Interval Type-2 TSK Fuzzy Logic System , 2013 .

[23]  Jessica J. Fridrich,et al.  Design of adaptive steganographic schemes for digital images , 2011, Electronic Imaging.

[24]  Lin Jia-jun,et al.  HVS-Based Imperceptibility Evaluation for Steganography , 2009, Infoscale.

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

[26]  Gwoboa Horng,et al.  A novel secret image sharing scheme for true-color images with size constraint , 2009, Inf. Sci..

[27]  A.A. Abdelwahab,et al.  A discrete wavelet transform based technique for image data hiding , 2008, 2008 National Radio Science Conference.

[28]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[29]  Michael A. Pratt,et al.  Texture-based image steganalysis by artificial neural networks , 2008, Int. J. Intell. Comput. Cybern..

[30]  Nameer N. El-Emam,et al.  New steganography algorithm to conceal a large amount of secret message using hybrid adaptive neural networks with modified adaptive genetic algorithm , 2013, J. Syst. Softw..

[31]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[32]  Laiha Binti Mat Kiah,et al.  Increased capacity of image based steganography using artificial neural network , 2012 .

[33]  Rinita Roy,et al.  Optimization of Stego Image Retaining Secret Information Using Genetic Algorithm with 8-connected PSNR , 2015, KES.

[34]  Fan Zhang,et al.  The upper and lower bounds of the information-hiding capacity of digital images , 2008, Inf. Sci..

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

[36]  Rini Akmeliawati,et al.  Online sequential extreme learning machine algorithm based human activity recognition using inertial data , 2015, 2015 10th Asian Control Conference (ASCC).

[37]  Longbing Cao,et al.  T2FELA: Type-2 Fuzzy Extreme Learning Algorithm for Fast Training of Interval Type-2 TSK Fuzzy Logic System , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[38]  Rainer Böhme,et al.  Revisiting weighted stego-image steganalysis , 2008, Electronic Imaging.

[39]  Jun Kong,et al.  A novel image hiding approach based on correlation analysis for secure multimodal biometrics , 2010, J. Netw. Comput. Appl..

[40]  Chi-Man Vong,et al.  Sparse Bayesian Extreme Learning Machine for Multi-classification , 2014, IEEE Transactions on Neural Networks and Learning Systems.