Steganography View

A MODULO BASED IMAGE STEGANOGRAPHY ALGORITHM AGAINST STATISTICAL STEGANALYSIS

V.Nagaraj Pondicherry Engineering College

Outline • • • • • • • • •

Objective Steganography model Existing method Existing methods advantages and disadvantages Steganalysis Proposed method Tools and measures Project schedule References

Objective • Objective To propose a Modulo based LSB Steganography algorithm that can effectively resist image Steganalysis . • Steganography Process of hiding of a secret message within cover message and extracting it at its destination.

Steganography model Stego Key (K)

Stego Key (K)

Cover message (C)

Stego Function fE

Stego Inverse Function fE-1 Stego-message (S)

Secret Message (E)

Secret Message (E) Sender

Recipient

The Stegosystem

Cover message (c)

Existing Steganography method (Least Significant Bit) • The difference between two colors that differ by one in either one red, green or blue value is impossible to see with the human eye. • Image with gray values of 1 pixel:

248 + 201 + 3 = Orange Color 11111000 11001001 00000011 • Now the hidden message in the image 11111000 11001001 00000010

Existing Least Significant Bit method

Existing LSB method advantages and disadvantages • Advantages -- Does not change the size of the file. -- Is harder to detect than other steganography techniques. • Disadvantages -- If the picture with the hidden information is converted to another format, then the hidden data may be lost. -- Statistical Analysis method can be used to possibly identify a file with a hidden message.

Steganalysis • Process of identifying the Steganography message but difficult to extracting the secret message. • Methods of detecting – Visual Detection (JPEG, BMP, GIF, etc.) – Statistical Detection or Histogram Analysis – Structural Detection -View file properties/contents -size difference -date/time difference -color modifications

Proposed Modulo based LSB Steganography • Function of Combing every 2 samples of LSB bits using addition mod 2 or m to form the value which is compared to the part of the secret message. • Gray value of pixel(x) and size of embedded message(n) • Then calculate function n=l/m Where – ‘l’ is the length of bit stream of embedded message. – ‘m’ is the number of bits used to embed message in each pixel.

LSB Modulation process • Embed message like

xi= xi + ei - (LSBm (xi-1 )+ LSBm(xi)) mod2m Where – x is the gray value, and – e is the embedded value. • Extracted like below

ei - (LSBm (xi-1 )+ LSBm(xi)) mod2m Where –x is the gray value, and – e is the embedded value.

Tools and Measures • Software tools used – Xnview software – Mat lab 7 – VB 6 • Performance Metrics – SNR(signal to noise ratio) – Histogram Analysis

Project Schedule • Phase 1  Module 1 - Literature survey of Steganography and LSB techniques.

 Module 2 - Proposed Modulo Based LSB Steganography Algorithm.

 Module 3

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Comparing Performances of Measures with Existing LSB method.

 Module 4 -Proposal of the improved Double layer LSB Steganography.

• Phase 2  Module 5 -Proposal of the Adaptive Based Steganography.

References [1] Hong-Juan Zhang, and Hong-Jun Tang, “A Novel Image Steganography Algorithm against Statistical Analysis”, Proc. of the IEEE, vol 19, no.22,pp.3884-3888,August 2007. [2] F. Peticolas , RJ. Anderson, and MG. Kuhn, “Information hiding-A survey”, Proc. of the IEEE, vol 87, no. 7, pp.1062-1078, June 1999. [3] W. Bender, D. Gruhl , N. Morimoto, and A. Lu, “Techniques for data hiding”, IBM Systems Journal, vol 35, no.3-4, pp.313-336, December1996. [4] H. Farid, “Detecting hidden messages using higher-order statistical models”, International Conference on Image Processing, October 2002. [5] J. Fridrich, M. Goljan, and R. Du, “Detecting LSB Steganography in Color and Gray-Scale Images”, Magazine of IEEE Multimedia Special Issue on Security, pp. 22-28, November 2001. [6] J. Fridrich,M. Goljan, “Practical Steganalysis of Digital Images – State of the Art”, Proc. SPIE, Photonics West, vol. 4675, Electronic Imaging 2002, Security and Watermarking of Multimedia Contents, San Jose, California, pp. 1-13, January 2002. [7] N. Provos , “Defending against Statistical Steganalysis”, In 10th USENIX Security Symposium, Washington, December 2001. [8] Gougelet Pierre-emmanuel.: XnView, Software available at http:// www.xnview.com.