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Bit level Block Based Steganography Technique using Edge Detection
1Manmeet Kaur, 2Jagminder Cheema Brar, 3Sukhveer Brar
1, 2 CSE, PTU, BMSCE, Sri Muktsar Sahib, Punjab, India
3 ECE, PTU, BMSCE, Sri Muktsar Sahib, Punjab, India
Abstract - Steganography is the art and science of hiding secret data information in other media like digital images. The secret message is hidden in such a way that no one can apart from the sender or the intended recipient view the information.
Therefore, different techniques have been proposed so far for.
Steganography can be proceeded either in spatial domain of image where the data is hidden in the pixels of the image without any modifications to the image or it can be transformed domain steganography where the image is transformed into the frequency domain before embedding the message.
Imperceptibility, Capacity, and Robustness are main characteristics features of any data hiding technique. So far many techniques have been proposed for edge based steganography. And these techniques have been achieved better performances than the conventional techniques of data hiding.
But quality of the stego image, data security, and data capacity are still major issues faced by the area. The key objectives of the research are to design a high capacity data embedding algorithm, to propose a block based bit level algorithm to improve the quality of the image, to find the improved imperceptibility, to make the algorithm simple secure and less time consuming. The present work introduces a Block based Steganography Technique which hides the message bits into the edges of color images. In the proposed algorithm, to preserve the quality of the stego image it is preferred to hide the data at the edges because by doing so the visual quality of the image is affected less as compared to the other areas in the image. And the parameters like MSE, PSNR, BER, and CC were calculated to check the imperceptibility of the algorithm. The results were tested using various images of various sizes by embedding the data of different lengths. The smaller values of MSE, high values of PSNR, values closer to zero for BER, and values closer to 1 for CC shows the high imperceptibility of the algorithm. Also due to use of the secret key the proposed algorithm is more secure than the conventional algorithms and an unauthorized person cannot access the secret message without knowledge of the secret key.
Keywords – Steganography, staganalysis, Canny, stego image
I. INTRODUCTION
teganography is the skill of hiding secret information imperceptibly in a cover medium. The phrase
“Steganography” is drawn from Greek origin and means invisible written data message. Hence, the core idea of steganography is to secrete the very existence of the message in the cover medium. Steganography comprise a vast collection of methods for secret communication which
conceal the very existence of hidden information. Traditional techniques include use of invisible inks, microdots etc.
Modern day generally steganographic methods try to take advantage of the digital images and video files etc
.
Due to advancement in internet, most of the people transfer their information on the internet. They use images, audios, videos to hide their information. The security of the information is the most challenging issue nowadays. So, to achieve the security of the information on the internet there is a great need of amelioration in the area of steganography. For hiding information from the intruder there is also need of quality of the image and the capacity that how much an image can hide information. So that intruder faces difficulty in detecting the information which is hidden inside the image.The main challenges of the area are to build a system with as high as possible data payload, can embed or hide the data which does not disturb the quality of the media in which the message is being embedded, so that media can be avoid from staganaylsis, and can be more secure for data transfer or information sharing. As discussed in the literature a lot of techniques have been proposed to achieve the desired challenges. But the work in the presented dissertation is processed keeping in view some of the problems identified
II. OBJECTIVES
The main objective of this dissertation is to study the application of image steganography to facilitate secure communication in self-communication, one to one communication and one-to-many communication. This thesis studies some innovative ways to enhance steganography in digital images. The objective of this thesis work is to build up and validate a novel approach to provide performance enhancements over the steganography methods proposed in the literature. The key objectives are:
a. To design a high capacity data embedding technique.
This will be implemented by increasing the number of edge pixels by using a hybrid edge detection technique. And hiding the data at edge as well as non edge pixels.
b. Block based bit level method of data hiding will be used to maintain the quality of the Image.
c. To evaluate the performance of the purposed algorithm by comparing it with existing First
S
www.rsisinternational.org Page 8 component alteration method or conventional LSB
method of data hiding.
III. METHODOLOGY
In the present work, a Block based Steganography Technique is proposed which hides the message bits at the edges of the cover image. The presented algorithm can be applied to the RGB images and does not hide the data to the gray scale images. The user of the system is asked to select the original image, secret data, and secret key. After having these inputs from the user the secret data entered by the user is hidden into the image selected by the user with the proposed algorithm 3.1. Message Hiding Algorithm Steps
Input: RGB Image, Secret Key, Secret Message Output: Stego Image
Step 1:Read RGB image file.
Step 2:Then divide image into blocks of four pixels.
Step 3:Read the status of last three pixels of the group as edge and non edge pixel.
Step 4:Hide their status in first pixel of the group.Hide 1 for edge pixel and 0 for non edge pixel.
Step 5:If the pixel is edge pixel then hide 3 bits of secret message into image blue component.
Step 6:Hide one bit at LSB of each layer if pixel is non edge.
Step 7:Re-arrange each block in last.
3.2 Message Retrieval Algorithm Steps
Input: Stego Image, Secret Key Output:Secret Message
Step 1:Read the Stego-image file.
Step 2:Then divide image into blocks of four pixels.
Step 3:Read the first pixel of the group and check the status of other threepixels.
Step 4:Identify weather the pixel is edge or non-edge pixel.
Step 5:If the pixel is edge pixel then retrieve three bits from the blue layer.
Step 6:Otherwise, retrieve one bit from each layer (i.e. Red, Blue and Green).
3.3 Image Edge Detection
The Canny method applies two thresholds to the gradient: a high threshold for low edge sensitivity and a low threshold for high edge sensitivity. Edge starts with the low sensitivity result and then grows it to include connected edge pixels from the high sensitivity result. This helps fill in gaps in the detected edges.The Canny method finds edges by looking for local maxima of the gradient of Image. The gradient is calculated using the derivative of a Gaussian filter. The method uses two thresholds, to detect strong and weak edges, and includes the weak edges in the output only if they are connected to strong edges. This method is therefore less
likely than the others to be fooled by noise, and more likely to detect true weak edges.
3.4 Data Embedding Capacity/Payload
The data capacity or payload of a Steganography algorithm is an indication of the amount of information bits that can be embedded into the digital media. The payload size varies with the application. Capacity is a fundamental property of any Steganography algorithm, which very often determines whether a technique can be profitably used in a given context or not. However no requirement can be set without considering the application the technique has to serve in.Possible requirements range from some hundreds of bits in security–oriented applications, where robustness is a major concern, through several thousands of bits in applications like captioning or labeling, where the possibility of embedding a large number of bits is a primary need.
Data Payload or Capacity for Proposed Algorithm
Data capacity or payload can be calculated by calculating the maximum message length to be hidden in the image as explained below.
Capacity = Maximum Message Length
Maximum Message Length = No. of Blocks in whole image X No. of bits/block
Number of Blocks = (No. of Rows X No. of Columns)/4 For example, for an image of size 512 X 512 the Message Length will be
Number of Blocks = (512 X 512)/4
= 65,536 blocks Number of bits per block = 9
Capacity=65536 X 9
= 5,89,824 bits
= 73,728 bytes
=72 KB
So for an image of size 512 X 512 a data of 72 KB can be embedded into the whole image.
IV. TABLES, FIGURES AND EQUATIONS 4.1 Comparison between Proposed Technique and Previous Techniques
Image
Data Length
(in bytes)
Previous Work [37] Proposed method
MSE PSNR MSE PSNR
Lena (512 X 512 X 3)
792 1.9044 45.3672 0.0006 79.812
1702 4.4395 41.6915 0.0016 76.181
2547 6.4501 40.0692 0.0031 73.164
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Pepper (512 X 512 X 3)
792 3.7463 42.4288 0.0015 76.388
1702 8.0184 39.1239 0.0034 72.806
2547 12.4848 37.2010 0.0045 71.567
Baboon (331 X 345 X 3)
792 3.3397 42.9277 0.0053 70.870
1702 6.7508 39.8712 0.0135 66.838
2547 9.4621 38.4049 0.0197 65.196
The comparison results show that our system performance is better than previous methods. It is very clear from the table values that for all the images the proposed method has better values of the parameters for any given amount of data clearing the high imperceptibility of the proposed method.
After hiding a very big amount of data of 2547 bytes the MSE of the proposed method is 0.0015 which is very small as compared to the value of 1.9044 achieved by the method in [37] after hiding the data of only 792 bytes. Also with this it is clear that the proposed algorithm can achieve high data payload without affecting the quality of original image.
4.2 Performance of Proposed Method using Standard Test Images
Figures 4.2.1 to 4.2.3 shows the cover images, edge images and corresponding stego images of Peppers after hiding a data of 792, 1702 and 2547 bytes respectively and calculated values of CC, MSE, PSNR and BER are tabulated in table 4.2
Fig. 4.2.1 Simulation Results of Peppers Image with 792 Bytes (a) Original Image (b) Edge Image (c) Stego Image
Fig. 4.2.2 Simulation Results of Peppers Image with 1702 Bytes (a) Original Image (b) Edge Image (c) Stego Image
Fig. 4.2.3Simulation Results of Peppers Image with 2547 Bytes (a) Original Image (b) Edge Image (c) Stego Image
Image
Data Length
(in bytes)
CC MSE PSNR BER
Peppers (512 X 512 X 3)
792 0.8580 0.0015 76.388 0.0131 1702 0.8436 0.0034 72.806 0.0137 2547 0.8399 0.0045 71.567 0.0140
4.3 Parameters and equations
As a measure of the quality of a Stegoimage, Bit Error Rate (BER), Peak Signal to Noise Ratio (PSNR),Mean Squared Error (MSE), and Correlation Coefficient (CC) is calculated between the original image and the corresponding Stego image.
Mean Squared Error (MSE): To measure the similarity between the original image and Stego image an error signal is computed by subtracting the Stegoimage from the original frame, and then computing the average energy of the error signal. The MSE is given by equation
MSE = 1
MN Mi=1 Nj=1(x i, j − y(i, j))2 (1) Where x(i, j) is represents the pixel values of original image and y(i, j) represents the corresponding pixel values of Stego image and i and j are the pixel position of the M×N image.
MSE is zero when x(i, j) = y(i, j)
Peak Signal to Noise Ratio (PSNR): The PSNR is evaluated in decibels and is inversely proportional the Mean Squared Error. It is given by the equation
𝑃𝑆𝑁𝑅 = 10 log10 255
𝑀𝑆𝐸 (2)
Higher the value of PSNR better is the quality of the Steganographed frame.
Bit Error Rate (BER): BER is the reciprocal of the PSNR.
𝐵𝐸𝑅 = 1
𝑃𝑆𝑁𝑅 (3)
The value of BER which is closer to zero represents more quality of the Stego image.
Correlation Coefficient: To describe the robustness of any Steganography algorithm the original image and stego image are matched. The similarity between these can be measured by using the correlation factor𝜌, which is computed using the following Equation:
𝜌 𝑤𝑜, 𝑤𝑟 = 𝑤𝑜𝑖𝑗∗𝑤𝑟𝑖𝑗
𝑁𝑗 =1 𝑀𝑖=1
𝑀𝑖=1 𝑁𝑗 =1𝑤𝑜𝑖𝑗2 𝑀𝑖=1 𝑁𝑗 =1𝑤𝑟𝑖𝑗2
(4)
Where 𝑤𝑜𝑖𝑗 is a pixel of originalimage and 𝑤𝑟𝑖𝑗 is a pixel of the recovered Stego image of size M X N.
The correlation factor ρ may take values between 0 and1. The value closer to 1 represents the more similarity between the original image and stego image.
www.rsisinternational.org Page 10 V. CONCLUSIONS
Steganography is an efficient technique of hiding sensitive information within the media. In research work we have used a Block based Steganography Technique on images to obtain secure stego-image in which the message bits are hide at the edges of the cover image is implemented and analyzed for color images.We have performed test on standard images with different sizes of message data to be hidden. To make the algorithm high data capacity embedding system, the edge pixels from every layer are calculated using canny edge detector. In this technique the image is divided into a block of four pixels, the 1st pixel store the status of the other three pixels and remaining three pixels store the information bits.
In the proposed algorithm, to maintain the quality of stego image it is preferred to hide data in edges because by doing so the visual quality of image affected less as compared to other areas of image. Table 5.8 shows that PSNR and MSE of proposed technique is higher than previous work. Our results show that proposed algorithm is better than simple LSB technique.
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