An Improved Invert Threshold Based LSB ...

International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 8, August 2016

An Improved Invert Threshold Based LSB Steganography Hassam MSCS, Department of Information Technology Hazara University Mansehra, Pakistan

Abdul Qadir Phd Scholar, Department of Information Technology Hazara University Mansehra, Pakistan

Arif Iqbal Umar Chairman, Department of Information Technology Hazara University Mansehra, Pakistan

Zakir Khan Department of Information Technology Hazara University Mansehra, Pakistan

Noor Ul Amin Department of Information Technology Hazara University Mansehra, Pakistan

1 2 3 authentication. While the techniques of Steganography techniques gives another platform for data protection. The steganography techniques works with audio, video, text and image in order to hide the data securely in a way that the attacker cannot even think about getting secure data because of highly secure steganography algorithms [2]. Steganography consists three parts i.e. cover file, user’s information and the stego file. The cover file is used to embed the data, user information is the secrete message of user while the stego file is the resulting file having the embedded data in it. [3].

Abstract—There are various file types like audio, video and image that holds information, when the art of steganography is applied in order to hide information in these files. The main security of the steganography depends on the usage of the suitable algorithm and imperceptibility of the cover file’s information. The study of previous literature portrays numerous techniques of steganography available. Simplest technique of Steganography is least significant bit steganography. This Technique interchanges the data with least significant Bit of original image. One, Two, Three and Four LSB has been used. In order to increase the security, Five bit, 6 Bit and 7 bit LSB techniques can also be used but the SNR is decreased also perceptibility of the user information is decreased in stege image. In the proposed work we have used threshold levels for hiding more secret information based on color intensity in the darker regions as compared to the brighter regions. After embedding the secret data we invert those bit layers which are nearest to the original layer bits. Using the inversion technique we improve SNR and quality of the image as compared to normal threshold based technique. Proposed technique also decreases the error rate in the pixels and image as well.

II.

RELATED WORK

The LSB is the lowest significant bit in an image’s byte value. The LSB image steganography takes the important data in the least significant bit of pixel value of the cover image. The benefits of this kind of embedding is that it is easy and less complicated to hide the bits of the message directly into original image and many techniques are used in this method [4]. Some of these techniques are as followed: A. Difference Expansion LSB Technique It is a simple and advantageous reversible data hiding technique used for digital images. Here the excessiveness in the digital content is found to gain reversibility. In this method of secret data could be hidden in two bits of cover image pixels [5].

Keywords-Steganography, LSB, Stego Image, Cover image, SNR, PSNR, Compliment, HVS I. INTRODUCTION The technique for communication of critical data by embedding information in the file by avoiding from the access from attacker is achieved by steganography. The phrase steganography is derived from two words Steganos and Graptos. Steganos means Covered and Graptos means Writing [1]. It is a big challenge to secure the secret information from attackers. There are numerous techniques to cryptography to secure the data to be accessed by the attackers for providing information integrity and the source

The major flaw in this method was that the quantity of information can be hidden in cover image and data carrying image have low visual quality. B. Hiding behind the Corners There are several techniques that do not pay attention to the information of original cover image. Hiding behind corners avoids taking the original information of the cover image

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International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 8, August 2016

[6]. This technique hides the information in the corners of the cover image that are thought the most beneficial hiding places in an image. The major flaw of hiding behind corners has low hiding capacity

used i.e. RGB that are used in LSB to hide secret information. In LSB the data can be hidden in blue color or in the whole of RGB [13-16]. J. Stego One Bit LSB In this method only one bit of a color out of three is changed where the secret information is hidden. This method of hiding the data is undetectable to the human eyes. The major flaw of this technique is that it has low hiding capacity [17].

C. LSB in GIF Images These are the most commonly used images to hide data on internet. In this technique the data is hidden in the least significant bit of the GIF image and due to this variation image colors could be changed [7]. The flaw of this technique is that its capacity of hiding the data is quite low and it can easily be exposed to the rivals by visual and statistical attacks.

K. Stego Two Bit LSB As RGB has three colors. This technique uses two LSB of one color is used out of three to hide the secret data. It proves beneficial in hiding the data as compared to one bit LSB. As it has more data storage capability than the former but it is less secure and can easily be attacked [16].

D. Edge-based LSB Techniques In this method the edges of image are utilized to hide the data. This technique hides the secret data in some specific edges instead of every pixel of image. According to this technology data is hidden in that area of image where value of pixels from their relative pixels is different [8].

L. Stego three Bit LSB In this technique three LSBs of one color is used to store the secret data. Here the data hiding capacity is much high than latter two but shortcoming of this technique is that it can be detected easily and the secret data is easily approached [16].

The advantage of this technique is that it cannot be exploited by illegitimate user and flaw is that it cannot store the data in high quantity.

M. Stego four Bit LSB In Stego four bit LSBs, four LSBs of one color are used to hide the secret information. It affects the quality of cover image though the storage capacity is too high, also it is easy to approach the hidden data and security risks to the data are increased [16].

E. Pixel Value Differencing and Modulus LSB Technique This technique finds the difference between two pixels and then it applies modulus function to find out the reminder. This technique used modulus function and pixels difference value for hiding data [9].

N. Stego Colour Cycle (SCC) In this method different bits of all the three colors are used in a cycle to hide the data more securely than only using one or two bits of one color. It is most advantageous and secure technique of hiding the data. Not only the data storage capability is more but also it is more secure because the attackers do not access to the pattern of bits of three colors [16].

The major drawbacks of this technique is that it has low hiding capacity. F. Image Interpolation LSB Technique In image interpolation least significant bit technique hides the secret data in the interpolation are [4, 10]. This technique used the complex region for hiding more data as compared to the smooth region. But the problem is that the concealed data can attract the attention of the attackers and be easily extracted.

O. Threshold LSB based Steganography It is latest data hiding technique that focuses on the specific threshold bits that are adjusted to carry the secret data in a signal. The data that is hidden is dependent upon the basis of threshold ranges [18, 19].

G. Edge-based Technique This technique used the pseudo random number generator (PRNG) for hiding the secret information. The area where the data is to be hidden is chosen by the PRNG [11]. It selects a sensitive place in cover image to hide the data in which comparatively more data can be embed.

These ranges can be varied for 1 to 4 bits. This technique provides the better security against the visual attacks but has less SNR, PSNR and Image Quality and High error rate as compared to proposed technique.

H. Neighbourhood Pixel Information Technique In this method the data is hidden according to the information of neighbouring pixel [12]. This technique possess the capacity to hide the data to higher level and also high peak signal to noise ratio value. The major flaw of this technique is that the secret information can be destroyed and attract the attention of the user.

III.

PROPOSED SOLUTION

In this research work we use threshold levels for hiding more secret information based on colour intensity in the darker regions as compared to the brighter regions. After embedding the secret data we invert those bit layers which are nearest to the original layer bits. Using the inversion technique we improve SNR and quality of the image as compared to normal threshold based technique. It also decreases the error rate in the pixels and image as well.

I. Least Significant Bit Substitution To hide the secret data in an image is the most commonly used technique in steganography. Here in this technique the data is hidden in a number of bytes in the pixels of an image. In cover file the data is hidden by changing some bits of bytes in image pixels. Three types of color bytes are

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International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 8, August 2016

A. Encoding Process The encoding process is represented by Figure 1. And process is as follow: 1. 2. 3. 4. 5. 6.

7. 8.

7. 8.

Write Secret Data to file End

Start Read Image Read Data Convert Data to Binary Split Every pixel from LSB 1 to 4 Accordingly Threshold For Every LSB a. Get Data Binary According to LSB b. Decide Data or Compliment c. Join Pixel Accordingly Write Stego Image End

Figure 2: Decoding Process of Proposed Algorithm IV. EXPERIMENTAL RESULTS For the implementation of the proposed technique, four images used as shown in Figure 3. The specifications of these figures are: Grey level Lena 512x512x8; Colour Lena 512x512x3; Grey level Baboon 512x512x8; and Colour Baboon 512x512x3. Motivation behind the selection is their wide use among the communities of steganography ([1, 3, 20, 21]. Figure 1: Encoding Process of Proposed Algorithm

The secret message used in the proposed technique is a long bit of stream. MATLAB is used to implement the proposed technique, stego image is produced, as depicted in Figure 1 and Figure 2. The corresponding stego images for the above four cover images produced are shown in Figure 4.

B. Decoding Process The decoding process is represented by the figure 2. And the process is as follow 1. 2. 3. 4. 5. 6.

Start Read Stego Image Initialize Data Read Key Split Every pixel from LSB 1 to 4 Accordingly Threshold For Every LSB a. Get Data Binary According Key b. Join Data Accordingly

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International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 8, August 2016

shows that these values improved. The difference of error parameters are also shown in tables, the down-arrow shows that the error rate is decreased as compare to base technique. TABLE 1. LENNA GRAY RESULTS

TABLE 2.

LENNA COLOR RESULTS

(d) Baboon Colour

Figure 3. Cover Images

TABLE 3.

BABOON GRAY RESULTS

Parameaters

Proposed

TABLE 4.

43.6654 99.4770 19.5612 1.0035 0.1547 23.0000 0.0078 2.8180 1.6787

Difference 0.6431 0.0020 0.6431 -0.0091 -0.1471 -8.0000 -0.0001 -0.3879 -0.1198

BABOON COLOR RESULTS

Error

Proposed Threshold Difference 47.3503 45.8343 1.5161 99.6420 99.5760 0.0660 23.2456 21.7295 1.5161 0.6928 0.7468 -0.0540 0.0737 0.1736 -0.0999 12.0000 21.0000 -9.0000 0.0053 0.0058 -0.0004 1.2063 1.7102 -0.5040 1.0983 1.3078 -0.2095

Quality

Parameaters Peack Signal to Noise Ratio Universal Image Quality Index % Signal to Noise Ratio Mean absolute error MAE Average Differance Maximum Differance Normalize Absolute Error Mean Square Error Root Mean Square Error

The Figure 5 and Figure 6 shows the error and quality of the proposed technique. The graphs shows the quality is improved and the error rate is reduced for the proposed technique as compare to threshold based technique.

(c) Baboon Grey (d) Baboon Colour Figure 4. Stego Images

V.

Threshold

Error

(b) Lena Colour

44.3085 99.4790 20.2043 0.9944 0.0076 15.0000 0.0077 2.4301 1.5589

Quality

Peack Signal to Noise Ratio Universal Image Quality Index % Signal to Noise Ratio Mean absolute error MAE Average Differance Maximum Differance Normalize Absolute Error Mean Square Error Root Mean Square Error

(a) Lena Grey

Proposed Threshold Difference 43.1442 39.7747 3.3695 96.0470 94.9850 1.0620 19.0448 15.6753 3.3695 1.0866 1.4460 -0.3594 0.0402 0.5261 -0.4859 10.0000 15.0000 -5.0000 0.0088 0.0117 -0.0029 3.1773 6.9025 -3.7252 1.7825 2.6273 -0.8448

Error

Parameaters Peack Signal to Noise Ratio Universal Image Quality Index % Signal to Noise Ratio Mean absolute error MAE Average Differance Maximum Differance Normalize Absolute Error Mean Square Error Root Mean Square Error

Quality

(c) Baboon Grey

Error

(b) Lena Colour

Proposed Threshold Difference 44.1179 43.1171 1.0008 96.4240 96.0600 0.3640 20.0185 19.0177 1.0008 1.0542 1.1303 -0.0761 0.0033 0.1343 -0.1310 15.0000 15.0000 0.0000 0.0085 0.0091 -0.0006 2.5392 3.1972 -0.6581 1.5935 1.7881 -0.1946

Quality

(a) Lena Grey

Parameaters Peack Signal to Noise Ratio Universal Image Quality Index % Signal to Noise Ratio Mean absolute error MAE Average Differance Maximum Differance Normalize Absolute Error Mean Square Error Root Mean Square Error

COMPARISON OF RESULTS WITH BASE TECHNIQUE

The results of the stego images vs. original images are shown in the tables 1:4. These tables shows the threshold based and proposed technique results. The additional column shows the difference of the threshold based and proposed technique, which shows the improvement in the signal quality as SNR, PSNR and UIQI. The up-arrows

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[3] Gupta, H., R. Kumar, and S. Changlani, Enhanced Data Hiding Capacity Using LSB-Based Image Steganography Method. International Journal of Emerging Technology and Advanced Engineering, ISSN, 2013: p. 2250-2459. [4] Thomas, P. Literature survey on modern image steganographic techniques. in International Journal of Engineering Research and Technology. 2013. ESRSA Publications. [5] Gupta, S., A. Goyal, and B. Bhushan, Information hiding using least significant bit steganography and cryptography. International Journal of Modern Education and Computer Science (IJMECS), 2012. 4(6): p. 27. [6] Hempstalk, K., Hiding behind corners: Using edges in images for better steganography. 2006. [7] Bender, W., et al., Techniques for data hiding. IBM systems journal, 1996. 35(3.4): p. 313-336. [8] Singh, K.M., et al. Hiding secret message in edges of the image. in 2007 International Conference on Information and Communication Technology. 2007. [9] Wang, C.-M., et al., A high quality steganographic method with pixel-value differencing and modulus function. Journal of Systems and Software, 2008. 81(1): p. 150-158. [10] Luo, L., et al., Reversible image watermarking using interpolation technique. Information Forensics and Security, IEEE Transactions on, 2010. 5(1): p. 187-193. [11] Luo, W., F. Huang, and J. Huang, Edge adaptive image steganography based on LSB matching revisited. Information Forensics and Security, IEEE Transactions on, 2010. 5(2): p. 201-214. [12] Hossain, M., S. Al Haque, and F. Sharmin. Variable rate steganography in gray scale digital images using neighborhood pixel information. in Computers and Information Technology, 2009. ICCIT'09. 12th International Conference on. 2009. IEEE. [13] Katzenbeisser, S. and F. Petitcolas, Information hiding techniques for steganography and digital watermarking. 2000: Artech house. [14] Kipper, G., Investigator's guide to steganography. 2003: crc press. [15] Rahman, S.M., Multimedia Technologies: Concepts, Methodologies. 2008. [16] Bailey, K. and K. Curran, An evaluation of image based steganography methods. Multimedia Tools and Applications, 2006. 30(1): p. 55-88. [17] Neeta, D., K. Snehal, and D. Jacobs. Implementation of LSB steganography and its evaluation for various bits. in Digital Information Management, 2006 1st International Conference on. 2006. IEEE. [18] Hakeem, A., et al., Threshold Based LSB Audio Steganography. [19] Zakir Khan, M.S., Muhammad Naeem, Toqeer Mahmood, Shah Nawaz, Noor Ul Amin, Danish Shahzad, Threshold based Steganography: A Novel Technique for improved Payload and SNR.

Figure 5: Lenna Color Error Reduction

Figure 6. Lenna Color Quality Improvement VI.

CONCLUSION

The new LSB technique embeds secret information in the cover image based on its pixel values. The idea of inversion of LSB is used for improving the quality and the reduction of errors. This idea is used for embedding information in the pixels of cover image. The thresholds set is used according to base paper. Which embed 4-bits, 3-bits, 2-bits and 1-bit of the secret information in the image according to Human Visualization System and colour intensity. The experimental results show that proposed technique has high PSNR, SNR and UIQI as compared to the base threshold LSB steganographic technique. The proposed technique reduce the error rate in the resultant signal as compare to threshold based technique. REFERENCES [1] Hussain, M. and M. Hussain, A survey of image steganography techniques. 2013. [2] Hariri, M., R. Karimi, and M. Nosrati, An introduction to steganography methods. World Applied Programming, 2011. 1(3): p. 191-195.

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international Arab Journal of information technology, 2016. 13(4). [20] Altaay, A., S. bin Sahib, and M. Zamani. An introduction to image steganography techniques. in Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on. 2012. IEEE. [21] Khan, Z., et al. LSB Steganography using Bits Complementation. in International Conference on Chemical Engineering and Advanced Computational Technologies (ICCEACT). 2014.

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