Developing of modern communications

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1.INTRODUCTION:

Developing of modern communications need the special means of security mainly in computer network. The network security is becoming most important as the data is being exchanged on the internet as increases. The important things like confidentiality and data integrity are required to protect against unauthorised access .This made changes for the explosive growth of the field of information hiding area, which include copy right protection for digital media, watermarking, fingerprinting and Steganography. These are the different applications of information hiding. In watermarking applications, the message contains information such as owner identification and digital line. In fingerprinting, the owner of the data sets a serial number that uniquely identifies him as the owner .This serial number is added to copyright information and makes it possible to find the unauthorized usage of data set. Steganography hides the secret information with in the host data set and it is reliably communicated with the receiver [1]

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Steganography refers to the science of invisible communication .The general idea of hiding the information in digital context. The word Steganography is derived from the Greek words "steganos" meaning "covered or secret " and "grafy" meaning "writing or drawing" [1] defined it as "covered writing". The main goal of Steganography is to communicate securely in a completely and undetectable manner to avoid the drawing distrust to the transmission of the hidden data. In Steganography method if someone suspect that there is a secret information in a carrier medium and the method becomes worthless. Cryptography and Steganography are mostly used in the field of data hiding and received important attention from the both industry. Steganography provides high level secrecy and security by combining with Cryptography. Throughout the history this method is being followed by the ancients to communicate the information between people. Some examples followed by ancients on Steganography. In image Steganography the information is hidden mainly in images. The idea and practice of hiding information has a long history.

  1. In Histories the Greek historian Herodotus writes of a nobleman, Histaeus, who needed to communicate with his son-in-law and select messengers in Greece. He shaved the head of one of his most trusted slaves and tattooed the message onto the slave's scalp. When the slave's hair grew back the slave was dispatched with the hidden message. [2,14]
  2. In the Second World War the Microdot technique i.e invisible ink was used to write the information on pieces of paper so that the paper appeared to the average person as the blank paper. Liquids such as milk, vinegar and fruit juices were used because when each of these were heated and darken they become visible to human eye.[2]
  3. Information mainly concentrated on photographs, it was reduced in size until it was the size of a typed period. Extremely difficult to detect, a normal cover message was sent over an insecure channel with one of the periods on the paper containing hidden information it was developed by Germans.
  4. Another method is been used in Greece ,was where someone would peel wax off a tablet that was covered in wax, write a message underneath the wax and then they reapply the wax. The receiver of the message would simply remove the wax from the tablet to see the message.

Today Steganography is mostly used on computers with digital data being the carriers and networks being the high speed delivery channels. Steganography differs from cryptography in the sense that where cryptography focuses on keeping the contents of a message secret. Steganography focuses on keeping the existence of a message secret [3]. Steganography and Cryptography are both ways to protect information from unwanted parties but neither technology alone is perfect and can be compromised. Once the presence of hidden information is revealed or even suspected, the purpose of Steganography is partly defeated [3]. The strength of Steganography can thus be improved by combining it with Cryptography.

The goal of Steganography is to embed a message M in a cover object C in a covert manner such that the presence of the embedded M in the resulting stego object S cannot be discovered by anyone except the intended recipient. Steganographic applications only require the flexibility to alter C in order to be able to embed the hidden information. For this reason any type of digital object can be potentially used as a cover. For example, images, audio, streaming data, software or natural language text have been used as cover objects . Figure 1 the model of steganography, otherwise cryptography is the science of using mathematics to encrypt and decrypt data.[1]

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Two other technologies that are closely related to Steganography are Watermarking and Fingerprinting [4]. These technologies are mainly concerned with the protection of intellectual property, thus the algorithms have different requirements than Steganography. In watermarking all of the instances of an object are "marked" in the same way. The kind of information hidden in objects when using watermarking is usually a signature to signify origin or ownership for the purpose of copyright protection . With fingerprinting on the other hand, different, unique marks are embedded in different copies of the carrier object that are supplied to different customers. This enables the intellectual property owner to identify customers who break their licensing agreement by supplying the property to third parties . In watermarking and fingerprinting the fact that information is hidden inside the files may be public knowledge sometimes it may even be visible , while in Steganography the imperceptibility of the information is critical [3]. A successful attack on a Steganographic system consists of an rival observing that there is information hidden inside a file, while a successful attack on a watermarking or fingerprinting system would not be to detect the mark, but to remove it [4].Research in Steganography has mainly been driven by a lack of strength in cryptographic systems. Many governments have created laws to either limit the strength of a cryptographic system or to prohibit it altogether , forcing people to study other methods of secure information transfer[14]. Businesses have also started to realise the potential of Steganography in communicating trade secrets or new product information. Avoiding communication through well known channels greatly reduces the risk of information being leaked in transit [5]. Hiding information in a photograph of the company is less suspicious than communicating an encrypted file.

Conversely to image Steganography, we have image Steganalysis and it is a process or a way to detect or to estimate hidden information from the given image. Thus image Steganalysis is an art of discovering and rendering useless to hidden messages, hence breaking Steganography. Steganalysis is broadly divided in to two categories: passive and active Steganalysis. Passive Steganalysis detects the absence or presence of a secret message in an observed image or recognize the type of embedding algorithm. The active Steganalysis extracts/estimates some properties of the message of the embedding algorithm [6]. To achieve the information security and confidentiality, the secret information that is embedded in a carrier through random permutation of with the verification code .The permuted verification code is used to check or verify the secret information that is extracted from the received stego image.

2.Overview of Steganography

To provide an overview of Steganography, concepts and practises should first be explained.

In this section the concepts and definitions used in the field of Steganography. Firstly starting with the framework in which Steganography is mainly presented with some definitions. The modern formulation of Steganography given in terms of the prisoner's problem, where Alice and Bob are two inmates who wish to communicate each other in order to hatch an escape plan. All the communication is being examined by Warden, Wendy, who will put them in a solitary confinement at the slightest suspicion of hidden communication.

Particularly, in the general model for Steganography, Figure 1, we have Alice wishing to send a secret message m to Bob. In order to do so, she "embeds" m into a cover-object c, to obtain the stego objects. The stego-object s is then sent through the public channel.

Thus following definitions :

Cover -object(c): Refers to the object used as a carrier to embed messages into. Many different objects have been employed to embed messages in to examples images, audio, video as well as file structures and html pages to name a few.

Stego- object: Refers to the object which is carrying the hidden message .So given a cover object ,and a messages the goal of the Steganographer is to produce the stego image which will carry the message.

In a pure Steganography framework, the technique for embedding the message is unknown to Wendy and shared as a secret between Alice and Bob. However, it is generally not measured as good practice to rely on the secrecy of the algorithm itself. In private key Steganography Alice and Bob share a secret key which is used to embed the message. The secret key, for example, can be a password used to seed a pseudo-random number generator to select pixel locations in an image cover object for embedding the secret message (possibly encrypted). Wendy has no knowledge about the secret key that Alice and Bob share, although she is aware of the algorithm that they could be employing for embedding messages. In public key Steganography, Alice and Bob have private-public key pairs and know each other's public key.The warden Wendy who is free to examine all messages exchanged between Alice and Bob can be passive or active. A passive warden examines the message and tries to determine if it potentially contains a hidden message. If it appears that it does, she suppresses the message and/or takes proper action, else she lets the message through without any action. An active warden,[8] on the other hand, can alter messages intentionally, even though she does not see any trace of a hidden message, in order to foil any secret communication that can nevertheless be occurring between Alice and Bob. The amount of change the warden is allowed to make depends on the model being used and the cover-objects being employed. For example, with images, it would make sense that the warden is allowed to make changes as long as she does not alter significantly the individual visual quality of a suspected stego-image.[7]

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Cropping or any other form of image manipulation destroys the metadata. Finally, metadata can only be attached to an image as long as the image exists in the digital form and is lost once the image is printed. Information hiding allows the metadata to travel with the image regardless of the file format and image state (digital or analogue). A special case of information hiding is digital watermarking. Digital watermarking is the process of embedding information into digital multimedia content such that the information (the watermark) can later be extracted or detected for a variety of purposes including copy prevention and control. Digital watermarking has become an active and important area of research, and development and commercialization of watermarking techniques is being essential to help address some of the challenges faced by the rapid explosion of digital content. The key difference between information hiding and watermarking is the absence of an active challenger. In watermarking applications like copyright protection and authentication, there is an active adversary that would attempt to remove, invalidate or forge watermarks. Unlike information hiding and digital watermarking, the main goal of Steganography is to communicate securely in a completely undetectable manner. That is, Wendy should not be able to distinguish in any sense between cover-objects (objects not containing any secret message) and stego-objects (objects containing a secret message). In this context, Steganalysis refers to the body of techniques that aid Wendy in distinguishing between cover-objects and stego-objects. Wendy has to make this difference without any knowledge of the secret key which Alice and Bob may be sharing and sometimes even without any knowledge of the specific algorithm that they might be using for embedding the secret message. Hence Steganalysis is inherently a difficult problem. However, it should also be noted that Wendy does not have to bring together anything about the contents of the secret message m. Given the large number of digital images, and high degree of redundancy present in a digital representation of an image (despite compression), there has been an increased interest in using digital images as cover-objects for the purpose of Steganography. The development of techniques for image Steganography and the wide-spread availability of tools and increased interest in Steganalysis techniques for image data . Many of such techniques are specific to different embedding methods and to detect them. Instead we focus on some general concepts and ideas that apply across different techniques and cover-media.

3 Different kinds of Steganography

Almost all digital file formats such as Text images Audio , video, Protocol can be used for Steganography, but the formats that are more suitable are for those with a high degree of redundancy. Redundancy can be defined as the bits of an object that provide accuracy far greater than necessary for the object's use and display [9]. The redundant bits of an object are those bits that can be altered without the alteration being detected easily [9]. Image and audio files especially comply with this requirement, whereas research has uncovered with other file formats that can be used for information hiding.

An obvious method to hide a secret message in every nth letter of every word of a text message. It is only since the beginning of the Text Images Audio, Video, Protocol Internet and all the different digital file formats that is has decreased in importance Text Steganography using digital files is not used very often since text files have a very small amount of redundant data. Given the increase of digital images, especially on the Internet, and given the large amount of redundant bits present in the digital representation of an image, images are the most popular cover objects for Steganography. To hide information in audio files similar techniques are being used as for image files. One different technique unique to audio Steganography is masking, which exploits the properties of the human ear to hide information discreetly. A faint, but audible, sound becomes inaudible in the presence of another louder audible sound. This property creates a channel in which to hide information. Although nearly equal to images in steganographic potential, the larger size of meaningful audio files makes them less popular to use than images . The term protocol Steganography refers to the technique of embedding information within messages and network control protocols used in network transmission . In the layers of the OSI network model there exist covert channels where Steganography can be used . An example of where information can be hidden is in the header of a TCP/IP packet in some fields that are either optional or are never used. [10]

A. Image Steganography

Images are the most used cover objects used for Steganography. In the area of digital images many different image file formats exist, most of them for specific applications. For these different image file formats, different Steganographic algorithms exist.

A.1 Image definition

To a computer, an image is a collection of numbers that constitute different light intensities in different areas of the image. This numeric representation forms a grid and the individual points are referred to as pixels. These pixels are displayed horizontally row by row. The number of bits in a colour scheme, called the bit depth, refers to the number of bits used for each pixel. The smallest bit depth in current colour schemes is 8, meaning that there are 8 bits used to describe the colour of each pixel. Monochrome and greyscale images use 8 bits for each pixel and are able to display 256 different colours or shades of grey. Digital colour images are typically stored in 24-bit files and use the RGB colour model, also known as true colour. All colour variations for the pixels of a 24-bit image are derived from three primary colours: Red, Green and Blue, and each primary colour is represented by 8 bits .

The absence of all colours yields black, shown as the intersection of the zero point of the three colour axis. Magenta is formed by the mixture of 100% red, 100% blue, and the absence of green; cyan is 100% green and 100% blue without any red, and 100% green and 100% red with no blue combine to form yellow. White is the presence of all three colours.

Most digital image applications today support 24-bit True Colour, where each picture element (pixel) is encoded in 24 bits, comprising the three RGB bytes as described in Figure 1. Other applications encode colour using 8 bits/pixel. These schemes also use 24-bit true colour, but utilize a palette that specifies which colours are used in the image. Each pixel is encoded in 8 bits, where the value points to a 24-bit colour entry in the palette. This method limits the unique number of colours in a given image to 256 (28). Thus in one given pixel, there can be 256 different quantities of red, green and blue, adding up to more than 16-million combinations, resulting in more than 16-million colours . Larger amount of colours that can be displayed, the larger the file size.

Colour palettes and 8-bit colour are commonly used with GIF (Graphics Interchange Format) and BMP (Bitmap) image formats. GIF and BMP are generally considered to offer lossless compression because the image recovered after encoding and compression is bit-for-bit identical to the original image.[14]

A.2 Image Compression

When working with larger images of greater bit depth, the images are likely to become too large to transmit over a standard Internet connection. In order to display an image in a reasonable amount of time, techniques must be incorporated to reduce the image's file size. These techniques make use of mathematical formulas to analyse and condense image data, resulting in smaller file sizes. This process is called compression [11]. In images there are two types of compression: Lossy and Lossless . Both methods save storage space, but the procedures that they implement differ. Lossy compression creates smaller files by discarding excess image data from the original image. It removes details that are too small for the human eye to differentiate , resulting in close approximations of the original image, although not an exact photocopy. An example of an image format that uses this compression technique is JPEG (Joint Photographic Experts Group) . Lossless compression,[13] on the other hand, never removes any information from the original image, but instead represents data in mathematical formulas [11]. The original image's integrity is maintained and the decompressed image output is bit-by-bit identical to the original image input . The most popular image formats that use lossless compression is GIF (Graphical Interchange Format) and 8-bit BMP (a Microsoft Windows bitmap file)[14] . Compression plays a very important role in choosing which steganographic algorithm to use. Lossy compression techniques result in smaller image file sizes, but it increases the possibility that the embedded message may be partially lost due to the fact that excess image data will be removed. Lossless compression though, keeps the original digital image together without the chance of lost, although is does not compress the image to such a small file size . Different steganographic methodologies have been developed for both of these compression types, will be discussed below later.

B Audio :

Audio encoding involves converting an analogue signal to a bit stream. Analogue sound voice and music is represented by sine waves of different frequencies. The human ear can hear frequencies nominally in the range of 20-20,000 cycles/second (Hertz, or Hz). Sound is analogue or continuous signal. Storing the sound digitally requires that the continuous sound wave be converted to a set of samples that can be represented by a sequence of zeroes and ones .

Analogue to digital conversion is capable by sampling the analogue signal (with a microphone or other audio detector) and converting those samples to voltage levels. The voltage, or signal, level is then converted to a numeric value using a scheme called pulse code modulation (PCM). The device that performs this conversion is called a coder-decoder, or codec. PCM provides only an approximation of the original analogue signal, as shown in Figure 4. If the analogue sound level, for example, is measured at a 4.86 level, it would be converted to a 5 in PCM; this is called quantization error. Different audio applications define a different number of PCM levels so that this "error" is nearly undetectable by the human ear; the telephone network converts each voice sample to an 8-bit value (0-255) while music applications generally use 16- bit values (0-65,535) [12]

Analogue signals need to be sampled at a rate of twice the highest frequency component of the signal so that the original can be correctly reproduced from the samples alone. In the telephone network, the human voice in carried in a frequency band 0-4000 Hz (although only about 400-3400 Hz is actually used to carry voice), therefore, voice is sampled 8000 times per second (an 8 kHz sampling rate). Music audio applications assume the full spectrum of the human ear and generally use a 44.1 kHz sampling rate [14]

The bit rate of uncompressed music can be easily calculated from the sampling rate (44.1 kHz), PCM resolution (16 bits), and number of sound channels (2) to be 1,411,200 bits per second. This would suggest that a one-minute audio file (uncompressed) would occupy 10.6 MB (1,411,200*60/8 = 10,584,000). [12]Audio files are, in fact, made smaller by using a variety of compression techniques. One obvious method is to reduce the number of channels to 1 or to reduce the sampling rate, in some cases as low as 11 kHz. Other codecs use proprietary compression schemes. All of these solutions reduce the quality of the sound.

C.TCP/IP:

Another digital carrier can be the network protocols themselves. Covert TCP by Craig Rowland, for example, forms covert communications channels using the Identification field in Internet Protocol (IP) packets or the Sequence Number field in Transmission Control Protocol (TCP)segments.[14]

4.Different methodologies:

Image Steganography techniques are divided into two groups:

  1. Image Domain
  2. Transform Domain

Image Domain is known as spatial domain technique , embed messages in the intensity of the pixels directly, while for Transform also known as frequency domain, images are first transformed and then the message is embedded in the image .

Image domain techniques include bit wise methods that apply bit insertion and noise manipulation and at times its characterised as "simple systems" [15]. The image formats that are most suitable for image domain Steganography are lossless and the techniques are typically dependent on the image format. Transform domain involves the manipulation of algorithms and image Transforms . These methods hide messages in more significant areas of the cover image, making it more robust, and secured [3]. Many transform domain methods are independent of the image format and the embedded message may survive conversion between Lossy and Lossless compression [14]. Thus to an attacker the fact that an image other that of JPEG format is being transformed between two entities could mention of suspicious.

There are number of image Steganographic algorithms proposed and it will be explained in categories according to the domain in which they are performed.

There are different requirements depending on the purpose of Steganography.

Capacity: It is an important factor in applications, when a lot of information should be embedded into a cover image, which is usually related to the current picture.

Imperceptibility: it is important when a secret communication occurs between two parties and the fact of a secret communication is kept to be secret.

Robustness: Watermarking, Fingerprinting and all copyright protecting applications, robust Steganographic method, i.e. where the embedded information cannot be removed without serious degradation of the image.

Embedding Process

Steganography embeds a secret message in a cover message, this process is usually Parameterized by a stego key, and the reading or detecting of an embedded information is possible only having this key. Figure 1 shows this process

Least Significant Bit Insertion:

Usually 8 bit or 24 bit files are used to store digital images. The earlier one provides more space for information hiding, though, it can be quite large. The coloured representations of the pixels are derived from three primary colours: Red, Green and Blue(RGB). 24-bit images use 3 bytes for each pixel, where each primary colour is represented by 1 byte. Using 24-bit images each pixel can represent 16,777,216 colour values. We can use the lower two bits of these colour channels to hide data, then the maximum colour change in a pixel could be of 64-color values, but this causes so little change that is undetectable for the human vision system. This simple method is known as Least Significant Bit insertion [16]. Using this method it is possible to embed a significant amount of information with no visible degradation of the cover image. Figure 2 shows the process.

Several versions of LSB insertion exists. It is possible to use a random number generator initialized with a stego key and its output is combined with the input data, and this is embedded to a cover image. The usage of a stego key is important because the security of a protection system should not be based on the secrecy of the algorithm itself, instead of the choice of a secret key. Figure 3 shows this process.

The LSB inserting usually operates on bitmap images. 'Steganos for Windows' and 'Wbstego' is LSB inserting software products which are able to embed data (in clear or encrypted format) in a bitmap image. The embedded data cannot be considered as a Watermark, because even if a small change occurs in a picture (cropping, Lossy compression, colour degradation) the embedded information will be lost , although the change which is occurred during the embedding process is invisible. The original bitmap picture which was used during the test was a picture 1024 - 768 pixel in size, with 16M colours (it is a standard test picture in image processing). When these pictures were modified all the embedded information was lost. These software's do not use any redundancies during embedding, the embedding process does not apply any error correcting codes. In this case the error correction and the redundancies are useful only if the image is modified in bmp format. If a Lossy compression technique is applied, usually all the LSB bits are lost, therefore all Embedded information is also destroyed [16].

The widely known algorithm is mainly based on modifying the Least Significant Bit(LSB) of images is known as LSB Technique. This technique makes use of the fact that the LSBs in an image could be thought of the random noise and changes to them would not effect any changes in image. Some algorithms change LSB of pixels by a chance, others can modify certain areas of images or just changing the last bit by incrementing or decrementing the pixel valve.

Fridrich et al. [18] proposed another approach for embedding in spatial domain. In there method, noise that statistically resemble common distortion, example ,scanner noise or digital camera noise is introduced to pixel by chance. The noise that is produced by a pseudo random noise generator using a shared key. A parity function is designed to embed and detect the message, message signal modulated by the generated noise, and then added to the digital image .The noise is kept at low levels such that it cannot be perceptible to human eye and susceptible to detection by computer analysis without access to original image.

Public Key Steganography

As another possible way the algorithm requires the pre existence of a shared secret key to select pixels which should be adjusted. In this case both the sender and the receiver must have this secret. Suppose that the communicating parties doesn't have the opportunity to agree a secret key, but one of them (e.g. Bob) has a private/public key pair, and his partner knows the public key. In the case of a passive warden Alice knowing Bob's public key encrypts her message with this key, embeds it in a known channel (known position in the cover media), and sends it to Bob. Bob cannot be sure whether the channel contains a hidden message, but he can try to decrypt the random-looking string-sequence with his private key, and check whether it is a message or not. Another approach is the cover image escrow scheme (or source extraction), where the extractor is required with the original cover image, and the cover image is subtracted from the stego image before the extraction of the embedded information. In this scheme, the user cannot read the embedded data, it is only possible to have the original unmodified picture, but these types of algorithms are characterized as robust against signal distortions.[16]

Transform Domain:

The destination extraction algorithms can be divided into two groups: spatial/time domain and transform domain techniques. In the former case information is embedded in the spatial domain in the case of images, and in time domain in the case of audio materials. The transform domain methods operate in the Discrete Cosine Transform(DCT), Fourier (FFT)or wavelet transform domains(DWT) of the host signal.[4]

These mathematical transforms convert the pixels in such a way, as to give the effect of "spreading" the location of the pixel values over part of the image [11]. The DCT transforms a signal from an image representation to a frequency representation, by grouping the pixels into 8 - 8 pixel blocks and transforming the pixel blocks into 64 DCT coefficients each [17]. A modification of a single DCT coefficient will affect all 64 image pixels in that block. The next step is the quantization phase of the compression. The human eye is fairly good at spotting small differences in brightness over a relatively large area, but not so good as to distinguish between different strengths in high frequency brightness. This means that the strength of high frequencies can be diminished, without changing the appearance of the image. JPEG does this by dividing all the values in a block by a quantization coefficient. The results are rounded to integer values and the coefficients are encoded using Huffman coding to further reduce the size [11].

The Patchwork algorithm (developed at the MIT) selects random pairs of pixels, and increases the brightness of the brighter pixel and decreases the brightness of the other. This algorithm shows a high resistance to most non geometric image modifications. If it is important to provide a protection against filtering attacks, then the information hiding capacity is limited.

High colour quality images are compressed usually using a Lossy compression method as, for example, in the case of JPEG images. In JPEG algorithm the pixels are first transformed into a luminance chrominance space. The chrominance is then down sampled because the HVS (Human Vision System) is less sensitive to chrominance changes than to luminance changes so the volume of the data is reduced. Discrete Cosine Transform is then applied on the groups of 8 - 8 pixels. The next step causes the most loss in the case of JPEG, where the coefficients are scalarly quantized because if we reduce the coefficients of higher frequencies to zero, the changes to the original image will cause only small changes that a human watcher could not detect under normal circumstances. The final steps are Lossless, when these reduced coefficients are also compressed and a header is added to the JPEG image. Steganographic applications usually operate after the quantization step, for example JPEG-JSTEG, and SysCoP(System for Copyright Protection). SysCoP uses a position sequence generator. The inputs of the this generator are the image data and user key, the output is a position sequence for selecting blocks where the code is embedded [16].The block consists in this case of 8 - 8 pixels, it can be near the block is a square in the image or distributed, where the pixels are randomly selected. A label bit is embedded through setting specific relationship among three quantized elements of a block, and the algorithm contains a checking mechanism to test whether the actual block is capable of or not to store this information, how big modification is needed to store one bit information among these pixels. A popular method in a frequency domain is to modify the relative size of two or more DCT coefficients in an image block, embedding one bit information in each block. The two coefficients should correspond to cosine functions with middle frequencies which mean that the information is stored in a significant part of the signal. The algorithm should be robust against JPEG compression, so the DCT coefficients with equal quantization values should be chosen, in such away to the quantization table of JPEG. In the frequency domain the embedding process can usually hide less information into pictures, there is not such an exact limit in the size of the embedded object as in the case of LSB insertion, where the number of pixels, and the colour depth determine the maximum size of the embedded data . In the case of a transform domain operation the embedding process can cause visible changes if the embedded data size is too big, and the limit where a given embedded data size does not change the visual properties of the image is image dependent.

5. Research methods:

K Y Youngran Park ,Hyunho Kang and K. Kobayashi [19], has proposed an image Steganography method which is used to verify the secret information that is embedded in a Spatial domain of the Cover image had been deleted, forged or changed by attackers. This method uses AC coefficients of the Discrete Cosine Transform (DCT) domain.

LIU Tong and QIU Zheng-ding [20], has proposed this method that hide the secret information or message into a openly accessed colour image by the quantization based Steganography. This method ensures the transportation of the secret information that do not attract the attention of illegal eavesdropper. In this approach the original RGB image was converted to YCbCr space to use the perceptual masking function of YCbCr components; since the marked strength is properly tuned according to the human visual system, the energy of the hidden image is less than the Just Noticeable Distance (JND) of image, so the perceptual quality of the watermarked image is not severely degraded. The embedded message could be reliably extracted without resorting to the original image.

Giuseppe Mastronardi, Marcello Castellano, Francescomaria Marino, [21], explored the effects of Steganography in different image formats i.e, BMP, GIF, JPEG and DWT-Coded. Using these different formats, an idea of how bits of textual secret information can be embedded without perceptually deteriorating the quality of the image and also show where to inject the embedding bits in order to achieve the best trade-off in terms of length of the textual message and preserved quality of the image.

R D Jiri Fridrich [22], has shown the analysation of the security of Least Significant Bit (LSB) embedding for hiding messages in high color-depth digital images. They have focused a particular steganographic attack that is stego only attack. The method enables to detect the presence of pseudorandom message randomly spread in a colour image with quite reliability based on statistical analysis of the image colours in the RGB Cube.

C.T.R.Lisa.M.Marvel [23], have presented a method of embedding information within digital image Steganography and used the combination of the techniques of spread spectrum communication, error control coding and Image processing. The secret information is embedded within the noise, which is then added to the digital Image. The noise is kept at low levels, such that it is not perceptible to human eye and susceptible to detection by computer analysis without access to the original image.

Neil. F. Johnson and Sushil Jajodia [24], have proposed to detect the existence of hidden message and also to find the place of the hidden information that was hidden in the cover image.

Po-YuehChen and Hung-Ju Lin [30,35], have proposed a which first the Discrete Wavelet Transformation is performed on the digital image , separates the overlapping blocks and then classifies the wavelet coefficients of these overlapping blocks into a several patterns. The secret message or information is embedded into the digital image by changing the coefficient patterns.

Constantine Manikopoulos, Yun-Qing Shi, Sui Song, Zheng Zhang, Zhicheng Ni, Dekun Zou [34], have proposed a Steganography detection system (SDS) and is applied to the detection of block DCT-based Steganography in gray-scale images, segmented into 8x8 blocks. The differences in the coefficients of the block DCT transforms of the watermarked and un watermarked images from the original are treated as features. The Steganography detection system utilizes statistical pre-processing, over an observation region of each image that generates feature vectors over the regions. These vectors are then fed into a simple neural network classifier.

R. Chandramouli [25], presented two algorithms: coordinated search and random search within the optimization framework. These covert channels are immense cause of security concern because they can be used to pass malicious messages. These messages could be in the form of computer viruses, spy programs, terrorist messages, etc.,

Ying Wang and Pierre Moulin [26], have proposed a steganalysis of a block-DCT image Steganography where information is hidden in 8 x 8 block-DCT coefficients. Because of the block structure of DCT embedding, pairs of neighbouring pixels within an 8 x 8 block have different statistics from those across two 8 x 8 blocks. Two histograms of pixel differences are computed (one for each Population), from which a Kolmogorov-Smirnov (K-S) binary hypothesis test is derived to decide whether a given image is a stegoimage or a cover-image. The non-stationary introduced by block-DCT Steganography reveals the existence of hidden information. The difference between the distributions for pixel pairs from one block and across blocks provides a measure for the detection of stego-images. The steganalysis method works better for smooth images than for noise-like images.

R. Chandramouli [27], has presented three possible hiders that arise from the information hiders perspective: The first one is the Complete information case in which the information hider exactly knows the probabilities {pj}nj = 1 where pj is the probability of the jth Steganalysis detector being employed. The second one is the Incomplete Information case in which the information hider does not know the pj values but only the ordering, say, p1 p2..pn(with out loss of generating). The last one is the No information case in which no knowledge of pj is available.

H Y Shaohui Liu and W Gao, [28], proposed a Steganalysis technique on the basis of the histogram analysis on the wavelet coefficients for the detection. The approach has given importance on the methods by which the secret message is embedded through quantizing wavelet coefficients. The image statistical features are the important clues to determine whether information is hidden or not in the carrier from the detection process.

Vidyasagar M. Potdor and Elizabeth Chang [29], presented a steganographic algorithm in which the information or the message to be hidden is embedded into the carrier by modifying the grey n level values of the grey scale image pixels. Thus the steganographic algorithm allows secret communication, to cover and recover hidden information within the Spatial domain of the image with low computational Complexity and high information hiding capacity.

H. S. Gopalkrishna Reddy Tadiparthi and S. Mukkamala,[31], have proposed a steganographic technique that uses animation to encode the message. The technique has a combination of the cryptography, error-control coding and ASCII text embedding into frames of animation to obtain a robust Steganography system. Cryptography in this system was used to encrypt the message before it is encoded in the animation, so that the Steganography has much higher complexity. The error control coding is used to increase the strength of the Steganography system so that the Steganogram can recover from attacks that do not destroy the minimum fidelity of the Animation. The only limitation of the proposed model was the capacity that requires a huge carrier to deliver a small amount of payload.

U. M. S. Onkar Dabeer, kenneth Sullivan and B. S. Manjunath [32], proposed a theory of hypothesis testing to the detection of the hidden image in the least significant bit (LSB) of the host image based on two types of tests, one is the universal method that has a certain asymptotic optimality properties. And the other method is based on knowledge or the estimation of the host probability mass function (PMF).

Li Zhi and Sui AI Fen [33], proposed a method called Gradient Energy Flipping Rate detection (GEFR). Through the analysis of the variation of the gradient energy, which results from the LSB Steganography in colour and gray scale image, the secret message embedded in the target image is detected, and the length of the embedded message is estimated.

6 REFERENCES:

  1. A .I Hashad and A.S Madhan , "A Robust Steganography Technique using Discrete Cosine Transform," in International Conference on Information Technology, pp. 255-264, April 2003.
  2. Jamil, T., "Steganography: The art of hiding information is plain sight", IEEE Potentials, 18:01, 1999.
  3. Wang, H & Wang, S, "Cyber warfare: Steganography vs. Steganalysis", Communications of the ACM, 47:10, October 2004.
  4. ANDERSON, R. J. - PETITCOLAS, F. A. P., "On The Limits of Steganography", IEEE Journal of Selected Areas in Communications, ISSN 0733-8716 , pp. 474-481, May 1998.
  5. Artz, D., "Digital Steganography: Hiding Data within Data", IEEE Internet Computing Journal, June 2001.
  6. J.L. Jun Cheng , Alex C. Kot and H. Cao, "Steganalysis of Data Hiding in Binary Messages," in ISCAS ,pp 4405-4408,May 2005.
  7. R ChandraMouli, Mehdi Kharrazi, and Nasir Memon, "Image Steganography and Steganalysis: Concepts and Practice" T. Kalker et al. (Eds.): IWDW 2003, pp. 35-49, 2004.
  8. Joachim J. Eggers and R. Bauml, Bernd Girod, "A Communications Approach to Image Steganography" Proceedings of SPIE Vol. 4675 Security and Watermarking of Multimedia Contents IV San Jose, CA., Jan. 2002.
  9. Anderson, R.J. & Petitcolas, F.A.P., "On the limits of Steganography", IEEE Journal of selected Areas in Communications, May 1998.
  10. Ahsan, K. & Kundur, D., "Practical Data hiding in TCP/IP", Proceedings of the Workshop on Multimedia Security at ACM Multimedia, 2002.
  11. "Reference guide": Graphics Technical Options and Decisions", http://www.devx.com/projectcool/Article/19997
  12. Johnson, N.F., Duric, Z. and Jajodia, S.G. "Information Hiding: Steganography and Watermarking - Attacks and Countermeasures." Norwell (MA): Kluwer Academic Publishers, 2001.
  13. Theodore Rappaport , "Wireless Communications Principles and Practices", 2nd Edition Prentice Hall, ISBN: 0130422320, Dec 31, 2001
  14. Kefa Rabah, "Steganography -The Art of Hiding Data", Information Technology Journal, ISSN 1682-6027 pp245-269, 2004.
  15. Johnson, N.F. & Jajodia, S., "Steganalysis of Images Created Using Current Steganography Software", Proceedings of the 2nd Information Hiding Workshop, April 1998
  16. Jozsef Lenti , "STEGANOGRAPHIC METHODS", PERIODICA POLYTECHNICA SER. EL. ENG. VOL. 44, NO. 3-4, PP. 249-258 (2000)
  17. Krenn, R., "Steganography and Steganalysis", Jan 2004. http://www.krenn.nl/univ/cry/steg/article.pdf
  18. J. Fridrich and M. Goljan, "Digital image Steganography using stochastic modulation," SPIE symposium on Electric Imaging ,San Jose, CA,2003.
  19. Youngran Park, Hyunho Kang, Kazuhiko Yamaguchi, and Kingo Kobayashi "Integrity Verification of Secret Information in Image Steganography" The 29th Symposium on Information Theory and its Applications (SITA2006), Nov.28- |Dec. 1, 2006
  20. LIU Tong ,QIU Zheng-ding, "A DWT based colour image Steganography Scheme,"ICSP'02 Proceedings, pp.1568-1571,Feb 2002.
  21. Giuseppe Mastronardi, Marcello Castellano, Francescomaria Marino, "Steganography Effects in Various Formats of Images.A Preliminary Study," International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, pp. 116-119, July 2001.
  22. R D Jiri Fridrich, M. Long, "Steganalysis of LSB encoding in colour images," pp 1279-1282,Jan 2000.
  23. C T R Lisa M Marvel and C G Boncelet , "Hiding Information in Images" pp 396-398,Mar 1998.
  24. Neil. F. Johnson and Sushil Jajodia, " Steganalysis :The investigation of Hidden Information," International Conference on Information Technology, pp 113-116 ,Jun 1998.
  25. R.ChandraMouli, "Web Search Steganalysis: Some Challenges and Approaches," in ISCAS, pp. 576-579, April 2004.
  26. Y. Wang and P. Moulin, "Steganalysis of Block DCT Image Steganography," in International Conference on Image Processing, pp. 339-342, Mar 2003.
  27. R. ChandraMouli, "On Information Hiding with Incomplete Information about Steganalysis," in International Conference on Image Processing, pp. 1161-1164, Apr 2004.
  28. H. Y. Shaohui Liu and W. Gao, "Steganalysis of Data Hiding Techniques in Wavelet Domain," in International Confernce on Information Technology, pp. 119-121, August 2004.
  29. Vidyasagar M. Potdor and Elizabeth Chang, "Gray Level Modification Steganography for Secret Communication," International conference on image Processing, pp 223-228, April, 2004.
  30. Po-Yueh Chen and Hung-Ju Lin, "A DWT Based Approach for Image Steganography," International Journal of Applied Science and Engineering. ISSN 1727-2394 pp 275-290,2006
  31. A. H. S. Gopalkrishna Reddy Tadiparthi and S. Mukkamala, "Defeating the Current Steganalysis Techniques," in International Conference on Information Technology, pp. 224-228, April 2004.
  32. U. M. S. Onkar Dabeer, kenneth Sullivan and B. S. Manjunath, "Detection of Hiding in the Least Significant Bit," in IEEE Transaction on Signal Processing, pp. 362-375, Oct 2004.
  33. L. Zhi and S.A. Fen, "Detection of Random LSB Image steganography," in International Conference on Information Technology, pp. 2113-2117, April 2004.
  34. Constantine Manikopoulos, Yun-Qing Shi, Sui Song, Zheng Zhang, Zhicheng Ni, Dekun Zou, "Detection of block DCT-based Steganography in gray-scale images," pp 355-358 ,Feb 2002.
  35. Chin-Chen Chang *Tung-Shou Chen Hsien-Chu Hsia, "An Effective Image Steganographic Scheme Based onWavelet Transformation and Pattern-Based Modification," Proceedings of the 2003 International Conference on Computer Networks and Mobile Computing (ICCNMC'03),2003