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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