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Wednesday 13 December 2017

AN IMPROVED IMAGE STEGANOGRAPHY BASED ON LEAST SIGNIFICANT BIT MATCHING REVISITED (LSBMR) USING SOBEL EDGE DETECTION

ABSTRACT
Image steganography is the science of hiding data for securing confidential communication and it is the most popular type of carrier to hold information. Many algorithms have been proposed to hide information into digital images. The least significant bit algorithm (LSB) as one of the algorithms proposed is widely used in message embedding. However, the robustness of the algorithm based on LSB is low. The hidden message is usually destroyed when some image operations like resizing, cropping and rotation are applied to the stego-image. To overcome this limitation, this work proposed an improved image steganography based on least significant bit matching revisited (LSBMR) using Sobel edge detection that withstands image operations like resizing, rotation and cropping. The proposed method employs 2-dimensional discrete cosine transformation (2D-DCT) to transform the detected edges of the cover image pixel value into its coefficient, embeds the secret message in the coefficients of the detected edges of the cover image which was implemented in Netbeans IDE. Experimental results produced better stego-image quality that is robust against multiple image operations such as resizing and cropping. The statistical steganalysis tools such as Virtual Steganographic Laboratory (VSL) and StegExpose cannot detect the presence of secret information in the stego-image. Also, the proposed system generated stego-image with Peak Signal to Noise Ratio (PSNR) that is an image quality metric of 68 decibels (dB) for 8000 bits of secret message as regards to the invisibility over the existing steganography technique.

CHAPTER ONE
INTRODUCTION
This chapter discusses the introductory part of this dissertation, which includes the background to the study, problem statements and research motivation, the research aim and objectives, research methodology, contribution to knowledge and finally the organization of the rest of the dissertation.
1.1 Background to the Study
Recently, people exchange information using the existing communication technologies such as the internet and huge volume of data transfer takes place via the plethora of services offered by the web. This information can be very sensitive and need to be protected against any attacker who tries to intercept them during the transmission stage. According to Ratnakirti et al., (2013), data over internet may be stolen, intercepted, illegally modified or even destroyed by an adversary resulting in intellectual property rights infringement, data loss, data leakage and data damage. Transmitting top secret information cannot be solely relied on the existing communication channels because the technologies are vulnerable to attacks Osama, (2005) and exchanged information can be detected relatively easily. Therefore, it is vital to protect the privacy and confidentiality of top secret message during its transit through the internet. To preserve the privacy and confidentiality of important data over the internet it must be provided with a metaphorical envelope such that its contents are revealed only to the intended receiver Ratnakirti et al., (2013) without arousing suspicions. Data hiding techniques such as steganography precisely aim at performing this task.
The steganography technique has been used many years ago to convey secret messages. For instance, Greek historian Herodotus was the first to document the usage of steganography to send messages (Aubrey, 1996). A slave was sent by his master to deliver a secret message tattooed on his scalp. After the message was tattooed, the slave waited until his hair grew back and concealed the message. The most popular steganographic methods between the 13th and 16th century involved written text. One method used a mask, a paper with holes, shared between the sender and recipient. The mask was simply put over the text and the message was revealed. Francis Bacon realized that two different fonts for each letter can be applied to embed binary representations of messages. Holub, (2014), stated that ―Brewster devised a very original technique in 1857, which was later used in several wars‖. Security of most of the previously mentioned methods was achieved only by assuming ignorance of the adversary. This is sometimes pejoratively called security through obscurity. The adversary did not attempt any targeted attack in the sense of modern steganalysis, instead they trained spies and secret services to obtain the necessary information by other means (Fridrich, 2009). Although steganography is an ancient subject, the modern formulation of it is often given in terms of the prisoner‘s problem proposed by Simmons (Patel and Gadhiya, 2015), where two inmates wish to communicate in secret to hatch an escape plan. All of their communication passes through a warden who will throw them in solitary confinement should he/she suspect any covert communication (Chandramouli et al., 2003).
The warden, who is free to examine all communication exchanged between the inmates, can either be passive or active. A passive warden simply examines the communication to try and determine if it potentially contains secret information. If she suspects a communication to contain hidden information, a passive warden takes note of the detected covert communication, reports this to some outside party and lets the message through without blocking it. An active warden, on the other hand, will try to alter the communication with the suspected hidden information deliberately, in order to remove the information (Anderson and Petitcolas, 1998). Figure 1.1 represents modern steganography. Generally steganography is known as ―invisible‖ communication of hiding secret messages into digital cover-media such that attackers will not be aware of the existence of the hidden messages (Micheal and Herbert, 2011). It is a mechanism that completely differs from cryptography. In fact, in cryptography the information is modified but still can be seen in this unreadable format once sent over the networks, whereas in steganography the information is simply embedded into a digital support and cannot be noticed as long as the quality of the carrier is not deteriorated (Zohreh and Jihad, 2014). Steganography hides information in a variety of multimedia carriers that include video clip, a digital image, an audio file or text called cover object. Once the information is embedded in any of the cover media it is called stego-object. If the cover is an image or video file, then the result of embedding the information in the cover is referred to as stego-image or stego-video respectively.
It is shown that images are excellent carriers to hide and exchange sensible information over networks (Rodrigues et al., 2004). Many algorithms have been proposed recently to hide information into images and preserve their quality. In this dissertation, we focus on image steganography algorithms because image used as a host object was observed to have low communication cost and availability of large number of redundant bits. An image consists of light luminance or pixels represented as an array of values at different points. A pixel consists of one byte or more. For example in 8-bit images each pixel consists of 1 byte (i.e., 8 bits). While each pixel in a 24-bit image is represented as three bytes representing the Red, Green and Blue (RGB) colors (Caldwell, 2003).
Image steganography has many applications, especially in today‘s modern, high-tech world. Most people on the internet have a concern about privacy and secrecy. For two parties, image steganography allows to communicate secretly (Kamred, 2014). For some morally-conscious people is allowed to safely whistle blow on internal actions (Sahar, 2015). Also, it allows for copyright protection on digital files using the message as a digital watermark. One of the other main applications for image steganography is for the high-level or top-secret documents transportation between international governments (Phad et al., 2012). In medicine, medical practitioners can embed some information such as name, comments or diagnosis of the patient into their medical imagery and exams. Then medical images can be of different types such as embedding information into ECG images (Ibaida et al., 2010). In military, not only the content of the communication but also the communication itself between agencies must be kept secret. Information hiding technique can be used when two or more agencies communicate via digital short radio (Jiang et al., 2009). In smart id‘s, information of the person is embedded inside their image for confidential information (Flores‐Escalante et al., 2012). In remote sensing, information can be hidden into some site images to provide secret only to authorized users (Wang and Niu, 2008). In e-commerce, registration information can be hidden in electronic papers that can be used to identify authentication based on steganographic techniques (Wang and Ye, 2010). Also, it can be used in several areas such as: printers, database systems, human rights organization and correcting media transition errors. There exist several embedding algorithms for image steganography both in spatial and transform domains. But most algorithms in the spatial domain are vulnerable to image processing operations such as cropping, resizing and rotation. These problems, however, are of utmost importance; therefore there is need for continued improvement.

1.2 Problem Statement

Many research works have been conducted on spatial domain image steganography-based algorithms. Least significant bit (LSB) replacement in the spatial domain is a well-known steganographic method. In this embedding scheme, only the LSB plane of the cover image is directly overwritten with the secret bit stream according to a pseudorandom number generator (PRNG). Subhedar and Mankar, (2014) stated that ―spatial domain steganography schemes achieve high embedding capacities, but they are vulnerable to small modifications that may result from image processing operations such as cropping, rotation, scaling and resizing‖. It was investigated that the robustness of the image steganography algorithm based on LSBMR using Sobel edge detection is very low. The hidden message was destroyed when some image operations such as cropping, resizing and rotation were applied to the stego-image. Hence, the need to design and implement an improved image steganography based on LSBMR using Sobel Edge Detection that is robust against any image operations and works in transform domain.

MSC Project Topics and Complete Thesis in Computer Science

AN IMPROVED IMAGE STEGANOGRAPHY BASED ON LEAST SIGNIFICANT BIT MATCHING REVISITED (LSBMR) USING SOBEL EDGE DETECTION

Department: Computer Science (M.Sc Thesis)
Format: MS Word
Chapters: 1 - 5, Preliminary Pages, Abstract, References, Appendix.
No. of Pages: 88

NB: The Complete Thesis is well written and ready to use. 

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