Research Journal of Information Technology
Year: 2013 | Volume: 5 | Issue: 3 | Page No.: 277-290
ABSTRACT
In this study, a steganographic run is espoused for images with LSB as a building block. Living in the world of advanced technologies where zillions of people get connected online every day, problem of cyber crimes has also become inevitable. Since innumerable digital files are exchanged, importance given to problem solving measures has also up risen. For the security of images sake, steganography has introduced many modus operandi which are implementable both commercially and domestically. Steganography involves the hiding of data such that it is invisible to the naked eye and it also aims at hiding the very existence of the message itself by a cover medium which could be image or audio or video. Depending on the need of the user, any type can be used. Image steganography is very prevalent among these three. The choice of the algorithm is driven by parameters such as veracity, sturdiness, capacity and availability. Modified LSB is used in this algorithm but with a different perspective that give adequate implication to all of the previously mentioned. The algorithm is rationalized by means of MSE and PSNR results.
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Received: April 29, 2013;
Accepted: May 16, 2013;
Published: August 06, 2013
How to cite this article
Rengarajan Amirtharajan, K. Mohamed Ashfaaq, A. Kingsly Infant and J.B.B. Rayappan, 2013. High Performance Pixel indicator For Colour Image Steganography. Research Journal of Information Technology, 5: 277-290.
URL: https://scialert.net/abstract/?doi=rjit.2013.277.290
URL: https://scialert.net/abstract/?doi=rjit.2013.277.290
INTRODUCTION
We are now living in a world where data is analogous to currency; precious, vulnerable and can be used by anybody. However if data were to be plundered and used, it would give rise to inequity and immorality thus unleashing anarchy in the world. It is therefore a necessity to preserve data and prevent it from being used unlawfully (Schneier, 2007). Integrity of data is very important especially when one is dealing with data that can alter the Geo-economics. Such data have to carefully deal with and should be safely kept away from hackers and espionages (Amirtharajan and Rayappan, 2012a-d; Bender et al., 2000). But with data being so extensive and obvious how would one achieve the security of stochastic data?
Back during the ancient times, symbols and encryptions were used to communicate secret messages; messages were converted to gibberish and then back to perceivable message (Kahn, 1996). Over time the art of cryptography evolved to Cipher texts. Scholars and Mathematicians came up with different methods of encryption of data to make the secret message inaccessible to interceptors. With the advancement of digital systems cryptology evolved and took various forms but so did the controversies (Schneier, 2007). Cryptography attracted lot of attention to itself. It facilitated privacy but failed to protect the communicating parties, it also an act of incrimination in some countries to use cryptography.
Steganography the idea “security through obscurity” was the brain child Johannes Trithemius in 1499 that answered the puzzling question (Stefan and Fabin, 2000). Although this technique is of Greek origin the first documented usage was in his first book Steganographia which was known as the book of magic. Steganography was an extrapolation of Cryptography. Steganography on the other hand concealed the data and its communicator (Amirtharajan et al., 2011, 2012; Cheddad et al., 2010; Hmood et al., 2010a, b; Janakiraman et al., 2012a, b; Padmaa et al., 2011; Thenmozhi et al., 2012). The digital Steganography involved a carrier medium “Cover” such as a document (Al-Azawi and Fadhil, 2010; Xiang et al., 2011; Yang et al., 2011), audio (Zhu et al., 2011), video (Al-Frajat et al., 2010) or an image (Chan and Cheng, 2004; Gutub, 2010; Hong et al., 2009; Luo et al., 2008, 2011; Zanganeh and Ibrahim, 2011; Zhao and Luo, 2012) which supported fractionation. The data to be embedded was the secret message and the medium after the embedment “the stego image” (Mohammad et al., 2011; Rajagopalan et al., 2012; Zaidan et al., 2010, 2011).
The choice of the algorithm is driven by parameters such as Integrity, robustness, capacity, availability (Amirtharajan and Rayappan, 2012a, b, c, d). The other factor that drives the choice of algorithm is the domain in which the data is encoded i.e. spatial domain (Thanikaiselvan et al., 2011) and frequency domain (Amirtharajan and Rayappan, 2012a, b, c, d; Cheddad et al., 2010). The counter attack called steganalysis (Qin et al., 2009, 2010), which tries to reveal the existence of confidential information (Xia et al., 2009). The other classification in information hiding exclusively for authentication and copyright protection is called watermarking (Zeki et al., 2011; Zhang et al., 2010).
The data to be covertly transmitted is embedded directly in the image pixels in the spatial domain. But in the case of transfer domain steganography secret bits are embedded into the coefficients values of the transform domain (Amirtharajan and Rayappan, 2012d).
Steganography has four main modules; they are as follows (Amirtharajan et al., 2011, 2012):
| • | A cover file which acts as the container/carrier for the secret message |
| • | A secret message that contains the confidential data |
| • | A key for encoding the secret message |
| • | A steganography algorithm or function to sneak the secret message inside the cover object |
Blend of these Stego output components creates the stego output which is then transmitted to the recipient. At the destination, using decoding routine undisclosed message is got back from the stego output.
There are three main characteristics of steganography. They are imperceptibility, capacity and its robustness (Zaidan et al., 2010). The total amount of confidential information that can be hidden in a Stego-image defines its capacity. Robustness is the limit of modifications an adversary would have to do before he can break the secret code and get the hidden message (Gutub, 2010). Imperceptibility is how well the secret image is hidden before an intruder finds out about the hidden message, i.e., invisibility to human eyes (Amirtharajan et al., 2011, 2012; Zhang, 2010). Studying the available methods and its characteristics, this study has been proposed to implement a method to improve the imperceptibility and complexity by adapting Pixel Indicator (Gutub, 2010; Padmaa et al., 2011) along with modified LSB to reduce distortion (Zhang, 2010).
PROPOSED METHODOLOGY
The study takes the fundamental concept of LSB substitution along with Pixel indicator method (Gutub, 2010; Padmaa et al., 2011) and follows the same but with different reckon. Two such methods are advised here; one with default indicator and other with cyclic indicator. LSB substitution offers enhanced quality and capacity. Separating LSB planes is not a usual way that too here it is limited for 3 planes. On the word of indicator, confidential data is embedded in data channels by modifying the cover image samples by ±1 and ±2. The schematic diagram and flowchart for this study is given in Fig. 1 and 2.
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| Fig. 1: | Block diagram for proposed method |
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| Fig. 2: | Flow chart for proposed method |
Algorithm for embedding
Method 1:
| • | Read the cover (C) and secret data to be embedded (D) | |
| • | Divide the cover image into R, G, B planes | |
| • | Take Red plane as default indicator and follow the below mentioned rules: | |
| • | If last two LSBs of the indicator is: | |
| • | 00 = No embedding | |
| • | 01 = embed in blue plane | |
| • | 10 = embed in green plane | |
| • | 11 = embed in both green and blue planes | |
| • | Separate 3 LSB planes in Green and Blue | |
| • | Take first bit of secret data and compare it with the first LSB of the data channel plane. If it matches then embed the bit | |
| • | Take the next two bits of secret data and compare it with the two LSBs. Only allow ±1 modification | |
| • | Take 3 bits of secret data and compare it with the three LSBs and allow ±2 modification | |
| • | Repeat the process till all the secret data is embedded | |
| • | Store the resultant image as stego image | |
| • | Generate the stego key for embedding and communicate it to the receiver | |
Method 2
Cyclic PI approach:
| • | Read the cover (C) and secret data to be embedded (D) | |
| • | Divide the cover image into R, G, B planes | |
| • | Cyclic indicator is selected in this method; that is if red is default indicator for pixel 1, then green is the indicator for pixel 2 and blue for pixel 3. The remaining two act as data channels. If last two LSBs of the indicator is: | |
| • | 00 = No embedding | |
| • | 01 = Embed in blue plane | |
| • | 10 = Embed in green plane | |
| • | 11 = embed in both green and blue planes | |
| • | Separate 3 LSB planes in Green and Blue | |
| • | Take first bit of secret data and compare it with the first LSB of the data channel plane. If it matches then embed the bit | |
| • | Take the next two bits of secret data and compare it with the two LSBs. Only allow ±1 modification | |
| • | Take 3 bits of secret data and compare it with the three LSBs and allow ±2 modification | |
| • | Repeat the process till all the secret data is embedded | |
| • | Store the resultant image as stego image | |
| • | Generate the stego key for embedding and communicate it to the receiver | |
Algorithm for extraction:
| • | Read the stego image |
| • | Split the image into R, G, B planes and then divide by 3 LSB planes |
| • | Select the indicator channel and data channel with respect to 2 methods |
Using stego key from embedding extract all the secret data bits.
RESULTS AND DISCUSSION
The algorithm is tested in MATLAB 7.1 with cover images as Baboon, Lena, Mahatma Gandhi and Kovil which are of 256x256x3. The obtained MSE and PSNR values are tabulated. In method 1, default indicator is RED. Hence, no embedding is done on that plane. As it is evident, Kovil image affords high PSNR of 69.7667 dB. Any stego image of higher 38dB is said to be a high quality one. All four images are said to be highly imperceptible, escaping naked eye attempt. Histograms also depict the same results. Cover images with their corresponding stego images and histograms for the subsequent cover and stego images for method 1 are shown in Fig. 3-5 and 6. Analytical results for method 1 are tabled in Table 1.
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| Fig. 3(a-c): | Method 1: (a) Cover, (b) Stego images for Lena and (c) Corresponding histograms for 3a |
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| Fig. 4(a-c): | Method 1: (a) Cover, (b) Stego images for Baboon and (c) Corresponding histograms for 4a |
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| Fig. 5(a-c): | Method 1: (a) Cover, (b) Stego images for Mahatma Gandhi and (c) Corresponding Histograms for 5a |
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| Fig. 6(a-c): | Method 1: (a) Cover, (b) Stego images for Kovil and (c) Corresponding histograms for 6a |
| Table 1: | MSE, PSNR values for method 1 |
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| Method 2: Cyclic PI approach | |
| Table 2: | MSE, PSNR values for Method 2 along with comparison |
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In method 2, cyclic indicator approach is adopted. That is if Red is indicator for pixel 1, then Green and Blue act as data channels. If Green is indicator for next pixel, Red and Blue are assigned data channels and if Blue is indicator for the third one, Red and Green are termed data channels and so on. Here Green channel offers good PSNR characteristics. Here, fracas is homogeneous in all the channels. This indeed increases the capability of entrenching and also maximizes imperceptibility. Of the two methods presented here, method 2 fulfills the anticipation and hence is the best method. Cover images with their corresponding stego images and histograms for the subsequent cover and stego images for method 2 are shown is Fig. 7-10. Analytical results for method 2 are tabled in Table 2.
This study is compared with two other eminent methods of steganography by considering the same payload viz., Simple LSB and cyclic indicator Pixel indicator routine. It is realizable from the table that of the 2 methods, proposed method shows mended upshots in all the channels. PSNR values are outstandingly high thus making it worthy for practical implementation since the stego outputs do not leave behind any vestige for beholders. In spite of having higher complexity than LSB and cyclic indicator method, this paper offers more than anticipated results and thus vouches security. Hence, when glancing through the comparison, the proposed technique is determined to be beneficial.
The algorithm is run against Chi-square and is justified for its creation shown in Fig. 11. The plot is between embedding probability and number of rows participated in the process.
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| Fig. 7(a-c): | Method 2: (a) Cover, (b) Stego images for Lena and (c) Corresponding histograms for 7a |
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| Fig. 8(a-c): | Method 2: (a) Cover, (b) Stego images for Baboon and (c) Corresponding histograms for 8a |
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| Fig. 9(a-c): | Method 2: (a) Cover, (b) Stego images for Mahatma Gandhi and (c) Corresponding histograms for 9a |
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| Fig. 10(a-c): | Method 2: (a) Cover, (b) Stego images for Kovil and (c) Corresponding histograms for 10a |
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| Fig. 11: | Chi-square test for Mahatma Gandhi as Stego image for method 2 |
It is vivid in the graph that even before fifty rows in the image, the probability becomes zero. What is more to the graph is that both the cover and the stego is concurred and there is nil deviation between the curves. In general, 1 is the probability for entire row embedding and 0 is for no embedding. Thus, this algorithm works really well and disguises the secret data’s subsistence. Hence, this study is robust against chi-square.
CONCLUSION
Each and every expertise put into use nowadays needs updating and modernization and should be user friendly. Not only the end product but also the technologies behind the screens are not exemptions. Keeping this fact in mind, this study presents one more updated cum more beneficial technique which can be exercised in image steganography. This study has taken conventional LSB substitution routine to bury the secret message in cover image. But before embedding, substitution means is uniquely modified which has turned the paper into completely distinct algorithm. Here choice of indicator is also left to the user making it more user-friendly. The study is absolved by the illustration of experimental results in addition to histograms and output stego images. Finally, the script is examined by chi-square attack whose results substantiate this wrap up. Hence, to conclude, this study has got everything needed for a perfect steganographic algorithm and sounds good for domestic and saleable application.
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