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Noise and Color Restoration: Application for Restoration and Steganalysis

This project focuses on developing a new steganalysis method for digital color images by extracting features using steerable Gaussian Filters Bank. The objective is to detect hidden messages and restore images affected by noise. The project includes building a comprehensive database of color images and implementing various preprocessing steps.

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Noise and Color Restoration: Application for Restoration and Steganalysis

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  1. Model of Noise and Color Restoration: Application for Restoration and Steganalysis Hasan ABDULRAHMAM Supervisors Marc CHAUMONT Philippe MONTESINOS 3rdyear 2015 / 2016 Start PhD. : 01 / 03 / 2014 07/01/2020

  2. Plan Thesis info. Introduction Objectives Databases Proposed Method Results Plan for future work

  3. ThesisInfo • Publications: • H. Abdulrahman, M. Chaumont, P. Montesinos, and B. Magnier, "Color Image Steganalysis Based on Steerable Gaussian Filters Bank" IH&MMSec'2016, in Proceedings of the 4th ACM workshop on Information Hiding and Multimedia Security, • http://ihmmsec.org/program/, Vigo, Galicia, Spain, 6 pages, June 20-22, 2016. Acceptance Rate =36.2%.  • H. Abadulrahman, M. Chaumont, P. Montesinos and B. Magnier, "Color Images Stegananalysis Using RGB Channel Geometric Transformation Measures", Wiley Journal on Security and Communication Networks (SCN) - Special Issue on Cyber Crime, http://onlinelibrary.wiley.com/doi/10.1002/sec.1427/abstract/,  Edited By: Hsiao-Hwa Chen and Hamid R. Sharif, Guest Editors: WojciechMazurczyk, Krzysztof Szczypiorski, Zoran Duric, Dengpan Ye, Wuhan University, 12 pages, ISSN 1939-0122, DOI 10.1002/sec.1427, 2016. • H. Abadulrahman, M. Chaumont, P. Montesinos, and B. Magnier. "Color Image Steganalysis using Correlations between RGB Channels", IWCC2015, The 4th International Workshop on Cyber Crime, http://stegano.net/IWCC2015/, co-located with 10th International Conference on Availability, Reliability and Security - ARES 2015, organized by W. Mazurczyk, K. Szczypiorski, and A. Janicki, Université Paul Sabatier, Toulouse, France, August 24-28, 2015, 7 pages, DOI 10.1109/ARES.2015.44. Before starting my PhD • H. Abadulrahman, S. Sadoonand M. Khador,"Cyber –crime through internet and the solution face it ", • Journal: Al-TAQANI - ISSN: 1818653X,  Volume 24 Issue 9, Pages:45-53, 2011. • H. Abadulrahman, A. Tuama and M. Mustafa" Colored Image Compression using discrete transformation for sine and cosine functions", Journal: Al_mustansiriyah J. Sci. Vol 21, No. 5 , 2010. • H. Abadulrahman."Fingerprint Recognition System Using Neural Network ", Journal: Al_Mansour Vol. 25, No3, • pages 76-81, 2007.

  4. Introduction • Noise in Digital Image: • Noise presents during image acquisition, coding, transmission, and processingsteps. • Classification of the source noise: • - Naturalnoise like the photons of natural light. • - Hardware noise, like the sensors, …

  5. Introduction • Steganography method: • A technique to hide a secret message in a digital media. by modifying bits within various digital media files (for example images, videos, audio's). The general model for Simmons' "prisoners problem".

  6. Introduction • Steganography parts: The basic model of steganography system.

  7. Introduction • Classifications of steganography techniques: • Steganography by cover selection. • The sender selects an image from a large set of available images • and applies to it a message [1]. Cover is always seems very natural. Low hiding capacity. • Steganography by cover synthesis. • The sender create the cover that used to hide a secret message [2]. Good security. Low hiding capacity. • Steganography by cover modification. • The sender modifies an existing cover in order to embed a message [3]. Very good security. Large hiding capacity. More popular.

  8. Introduction • Recently steganography has received a great attention from national security.

  9. Introduction • Steganalysis methods: • The art and science of detecting hidden message in digital media. • There are three types of models based on the behavior of steganalytic. (a) Passive warden (b) Active warden (c) Malicious warden

  10. Introduction • Hypothesis • Embed messages inside the digital media involves some slight changes in this media. • Steganalysis research aims to develop some methods, that are effectively able to detect these modifications. Where is the good position to hide a secret message in image ?.

  11. Introduction Example 1: Using gradient filter Vertical edges Feature extraction Normalize of the gradient Cover image Horizontal edges

  12. Introduction Example 2: Using Steerable filters bank Red channel Feature extraction Green channel Cover image Blue channel

  13. Objectives • Developing a new steganalysis method for digital color images. • Extract features by calculating correlation between RGB channels image. • UsingsteerableGaussianFilters Bank to extractfeatures. • Using Multi SteerableFilters Bank for color images.

  14. Building Database • In our proposed work, we built the database of color image very carefully depending on the CFA idea. Raw images of size (3900 x 2600) Images of size (512 X 512) Format PPM The preprocessing steps for building our database depend on the CFA idea.

  15. Building Cover Database • Databases color image size of 512 x 512 based on demosaickingmethods:- ψ Database 1: Start cropping from red channel pixel Patterned Pixel Grouping (PPG). Database 3: • Normal cropping • Bilinear Method (BIL). 10,000 25,000 Database 2: Start cropping from red channel pixel Mixed demosaicking method Database 4: • Normal cropping • Patterned Pixel Grouping (PPG). 10,000 25,000 Database 5: • Normal cropping • Adaptive Homogenity -Directed (AHD). 25,000 40,000 Database 6: • Normal cropping • Variable Number of Gradients  methods (VNG). 25,000 • Normal cropping • Mixed demosaicking methods ( 2500 images) from ( 3, 4, 5 and 6 DB). Database 7: 10,000 Database 8: • Size of 256 x 256 • Proposed for Deep learning. ψ We used database 1 in proposed method 1, 2.

  16. Steganography Methods • To hide a message in color image we usedthreeSteganography methods which are: • WOW steganography. [4] ( Wavelet Obtained Weights ) - 2012 • S-UNIWARD steganography. [5] (S-UNIversal WAvelet Relative Distortion) – 2013 • Synch-HILL steganography. [6] (Synchronizing Selection Channel ) - 2015 [4] V. Holub and J. Fridrich. Designing steganographic distortion using directional filters. In Information Forensics and Security, 4th International Workshop pages 234–239. IEEE, 2012. [5]  V. Holub, J. Fridrich, T. Denemark, Universal DistortionFunction for Steganography in an Arbitrary Domain, EURASIP Journal on Information Security, (Section:SI: RevisedSelectedPapers of ACM IH and MMS 2013. [6] T. Denemark and J. Fridrich. Improving steganographic security by synchronizing the selection channel. In Proc. Of the 3rd ACM Workshop on Inf. Hiding and Multimedia Security (IH&MMSec), Portland, Oregon, pages 5–14, June 2015.

  17. Stego Database • We built sevenstego databases for eachsteganographymethod:- • WOW steganography 10,000 10,000 10,000 10,000 10,000 10,000 10,000 Playload: 0.05 bpc Hide 1638 char. Playload: 0.2 bpc Playload: 0.01 bpc Hide 327 char. Playload: 0.4 bpc Playload: 0.5 bpc Hide 16384 char. Playload: 0.3 bpc Playload: 0.1 bpc Hide 3276 char. • S-UNIWARD steganography 10,000 10,000 10,000 10,000 10,000 10,000 10,000 Playload: 0.1 Playload: 0.5 Playload: 0.05 Playload: 0.2 Playload: 0.01 Playload: 0.4 Playload: 0.3

  18. Stego Database • Synch-HILL steganography 10,000 10,000 10,000 10,000 10,000 10,000 10,000 Playload: 0.1 Playload: 0.5 Playload: 0.05 Playload: 0.2 Playload: 0.01 Playload: 0.4 Playload: 0.3 Example: Cover image Embeddingmessage Stego image:

  19. Proposed Method 1 • Color Image Stegananalysis Using Correlations Between RGB Channels [7] • Our proposition is to enrich the SCRMQ1 with an inter-channel correlation composed of two sets of features:- • The first set, produced by color rich model, gives 18157 features. • The second set gives 3000 features obtained from the correlation of • different R, G, B channel gradients. • Thefinal dimensional vector 21157 features for each color image, • 10000 covers and 10000 stegos which are ready to enter in the classifier. • [7] H. Abadulrahman, M. Chaumont, P. Montesinos, and B. Magnier • "Color Image Steganalysis using Correlations between RGB Channels", • IWCC 2015, The 4th International Workshop on Cyber Crime, http://stegano.net/IWCC2015/, • co-located with 10th International Conference on Availability, Reliability and Security - ARES 2015, Université Paul Sabatier, Toulouse, France, August 24-28, 2015

  20. Proposed Method 1 The prepocessing steps to obtain Normalized Correlations between gradients of each channel.

  21. Proposed Method 1 Cover image Stego image

  22. Proposed Method 2 • Color Images Stegananalysis Using RGB Channel Geometric Transformation Measures*[8]. This method is a further extension of method 1: This method uses two types of features, computed between color image channels: Reflects local Euclidean transformations. Reflects mirror transformations. These geometric measures are obtained by the cosine and sine of gradient angles between all the color channels. • Thefinal dimensional vector 24157 features for each color image, • 10000 covers and 10000 stegos which are ready to enter in the ensemble classifiers. • [8]*H. Abadulrahman, M. Chaumont, P. Montesinos and B. Magnier, "Color Images Stegananalysis Using RGB Channel Geometric Transformation Measures", Wiley Journal on Security and Communication Networks (SCN) - Special Issue on Cyber Crime, http://onlinelibrary.wiley.com/doi/10.1002/sec.1427/abstract/,  Edited By: Hsiao-Hwa Chen and Hamid R. Sharif, Guest Editors: Wojciech Mazurczyk, Krzysztof Szczypiorski, Zoran Duric, Dengpan Ye, Wuhan University, 12 pages, ISSN 1939-0122, 2016.

  23. Proposed Method 2 Figure 3: Features of extraction: Sine of the gradients angles extracting information from the direction of the local rotation.

  24. Proposed Method 2 Cover image Stego image

  25. Proposed Method 2 • Results: • The comparison between steganalysis of the proposed method 1 , 2 • with color rich and CFARM steganalysis. S-UNIWARD Average Test Error WOW Average Test Error

  26. Proposed Method 2 • Results: • TheComparisonbetween steganalysis of the proposed method 1 , 2 • with color rich and CFARM steganalysis. Synch-HILL Average Test Error

  27. Results Clustering Steganographic Modification Directions for Color Components [9] Weixuan Tang, Student Member, IEEE, Bin Li, Member, IEEE, Weiqi Luo, Member, IEEE, and Jiwu Huang, Senior Member, IEEE Sun Yat-sen University - China Average Test Error [9] W. Tang, B. Li, W. Luo, and J. Huang. Clustering steganographic modification directions for color components. Signal Processing Letters,IEEE, Vol.23(No.2):197–201, Feb. 2016.

  28. FutureWork • In the future, we would like to extract features from color image • using Multi steerable filter bank to detect the corners.

  29. Reference [1] Yifeng Sun and Fenlin Liu. Selecting cover for image steganography by correlation coecient. In Proceedings of Second International Workshop on Education Technology and Computer Science (ETCS), volume 2,pages 159162. IEEE, 2010. [2] Ying Wang and Pierre Moulin. Perfectly secure steganography: Capacity, error exponents, and code constructions. IEEE Transactions on Information Theory, Special Issue on Security, 54(6), 2008. [3] Jessica Fridrich and Jan Kodovsky. Multivariate gaussian model fordesigning additive distortion for steganography. In Proceedings IEEE, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, BC, pages 29492953, May 2631, 2013. [4] V. Holub and J. Fridrich. Designing steganographic distortion using directional filters. In Information Forensics and Security, 4th International Workshop pages 234–239. IEEE, 2012. [5]   V. Holub, J. Fridrich, T. Denemark, Universal Distortion Function for Steganography in an Arbitrary Domain, EURASIP Journal on Information Security, (Section:SI: Revised Selected Papers of ACM IH and MMS 2013. [6] T. Denemark and J. Fridrich. Improving steganographic security by synchronizing the selection channel. In Proc. Of the 3rd ACM Workshop on Inf. Hiding and Multimedia Security (IH&MMSec), Portland, Oregon, pages 5–14, 2015. • [7] H. Abadulrahman, M. Chaumont, P. Montesinos, and B. Magnier "Color Image Steganalysis using Correlations between • RGB Channels", IWCC 2015, The 4th International Workshop on Cyber Crime, co-located with 10th International Conference on Availability, Reliability and Security - ARES 2015, Université Paul Sabatier, Toulouse, France, August 24-28, 2015 • [8] H. Abadulrahman, M. Chaumont, P. Montesinos and B. Magnier, "Color Images Stegananalysis Using RGB Channel • Geometric Transformation Measures", Wiley Journal on Security and Communication Networks (SCN) - Special Issue on • Cyber Crime, 12 pages, ISSN 1939-0122, 2016. [6] BOSS. http://exile.felk.cvut.cz/boss/BOSS_Final [7] Dresten database http://forensics.inf.tu-dresden.de/ddimgdb/ [8] Dave Coffin, Main page of dcraw , http://www.cybercom.net/~dcoffin/dcraw

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