Presented by:
Ritesh Pratap Singh and Neha Singh

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Abstract--Mobile phones are the most commonly used devices in today’s scenario. The Multimedia Messaging Service (MMS) has became very popular for sending messages containing multimedia objects such as images, audio, or video clips among mobile users. Alongside, the need for the secure communication became more imperative. Steganography is the most reliable technique for hidden communication. Hiding information, especially in images has been an alternative solution for secret communication.
In this paper a new Steganographic approach within a MMS is presented and discussed. There application in mobile environment is analyzed. The platform used here is MATLAB.
Keywords: Steganography, Multimedia Messaging Service (MMS), PN generator.
I. INTRODUCTION
The main motive of using Mobile phones has been the exchange of information between two users. The Short Messaging System was introduced with GSM mobile phones and it is very rapidly become popular among users [3]. The Multimedia Messaging System (MMS) offers the ability to send and receive multimedia content using a mobile phone. The most important factor of MMS has been the security of the information. Cryptography was created as a technique for securing the security of the communication and many different methods have been developed to encrypt and decrypt data in order to keep the message secret [4].
Although this technique have been used for concealing the content of the message secret. Unfortunately it is sometimes not enough to keep the contents of a message secret it may be also necessary to keep the existence of the message secret. The technique used to implement this, is also known as Steganography. Steganography concerns itself with ways of embedding a secret message into a cover object, without altering the properties of the cover object evidently. The embedding procedure is typically related with a key, usually called a stego-key.
Steganography is mainly applied to the media such as text, images, video clips, music and sounds.
The Multimedia Messaging System supports various types of media formats. We are mainly emphasizing on the images. For still images JPEG with JFIF should be supported by MMS Clients, while for bitmap graphics the supported formats are: GIF87a, GIF89a and PNG [2]. It’s an advanced messaging service that lets users send multiple media in one single message to one or more recipients. Nevertheless, there has been some constraints in concealing data in different media.
As we concern with the size of media: text could not exceed 30kb, an image must be below 100kB and a video must be smaller than 300kB [5]. Hiding images, text into an image is something more complicated and suitable medium for clandestine transmission where the existence of the message is itself is not distinguished, but the content is obscured, if the message size is very large.
This paper intends to offer a comparative study of the different algorithm used for steganography in MMS to illustrate the security potential of steganography for business and personal use. This reflection is based on a set of criterion that we have identified for image steganography in MMS.
The remainder of this paper is structured as follows; Section II gives the proposed method in general and differentiates between the earlier method used [2] of Steganography in MMS. In section III the performance of the most effective algorithm for image Steganography in MMS both in spatial domain and transform domain are discussed. In section IV the experimental results are discussed and finally in section V conclusion is reached.
II. THE PROPOSED METHOD
In this paper we introduced an improved method for hiding data in MMS. This method is used for the secure data transfer from a computer to a mobile phone to another mobile phone through data cable, Bluetooth, Infrared or MMS etc. In this method a message can hide in an image on a PC through a program written in MATLAB. The user on the other side may again send this message to the PC and
AKGEC JOURNAL OF TECHNOLOGY, vol. 1, no.1
while using the MATLAB program the user can retrieve the hidden message.
The same procedure was tested on a Nokia 3230 model to Sony Ericsson W200i model mobile phones and again
transmitted to the PC; finally the data was successfully accessed. This program can also be written in J2ME (Java 2 Micro Edition) programming language. The paper also
presents a quantitative evaluation of the model utilizing two different algorithms and can be utilized in different areas according to the need of users. The model is evaluated in terms of both the average reduction in peak signal-to-noise Ratio compared to the original cover image as well as the elapsed time of both the algorithms used [1].
Here we provide the metrics for perceptibility and robustness. The most rigorously defined metric is PSNR, given below in equation (1). The main reason for this is that no good rigorously defined metrics have been proposed that take the effect of the Human Visual System (HVS) into account. PSNR is provided only to give us a rough approximation of the quality of the Steganography.
For hiding the data in image part of MMS, the method used earlier [2] was LSB modification. In this method of Steganography would be to embed the message into the Least Significant Bit of the cover object. Given the extraordinary high channel capacity of using the entire cover for transmission in this method, a small object may be embedded multiple times. Even if most of these are lost due to attack, a single surviving data would be considered a success.
However, despite its simplicity LSB substitution bring a lot of drawbacks. Although it may survive transformation such as cropping, any addition of noise or lossy compression is likely to defeat the Steganography. An even better attack would be to simply set the LSB bits of each pixel to one; fully destroy the message with negligible impact on the cover object. Thus LSB modification proves to be a simple and lacks the basic robustness that Steganography applications require.
The technique which we have been used for Steganography in MMS is CDMA Spread Spectrum. This technique for Steganography purposes has aroused a lot of interest. In CDMA spread spectrum technique, the hidden data is spread throughout the cover image making it harder to detect. A system proposed by Marvel et al. combines spread spectrum communication, error control coding and image processing to hide information in images [6].
Spread spectrum communication can be defined as the process of spreading the bandwidth of a narrowband signal across a wide band of frequencies [6]. This can be accomplished by adjusting the narrowband waveform with a wideband waveform, such as white noise. After spreading, the energy of the narrowband signal in any one frequency band is low and therefore difficult to detect [6].
In spread spectrum image steganography in MMS the message is embedded in noise and then combined with the cover image to produce the stego image. Since the power of the embedded signal is much lower than the power of the cover image, the embedded image is not perceptible to the human eye or by computer analysis without access to the original image [6].
III. PEFORMANCE EVALUATION OF THE
PROPOSED SCHEME
In this paper we present a two new method for steganography in MMS messages. In the following, first, the steganography in MMS in the spatial domain is described. Then we will explain our second method for hiding the data in the MMS in the transform domain.
CDMA Spread Spectrum in Spatial Domain: This proposed technique for message embedding in images of MMS exploits the correlation properties of additive Pseudo-Random Noise patterns as applied to an image [7].
A pseudo random noise (PN) pattern W(x, y) if added to cover image I(x, y), to create a stego image according to the equation (2) given below.
In equation 2, k denotes a gain factor, and IW the resulting Steganographic image. Increasing k increases the robustness of the hidden message at the expense of the quality of the stego image.
The proposed technique utilizes the concept of CDMA spread spectrum, according to which we scatter each of the bits randomly throughout the cover image, increasing capacity and improving resistance to cropping.
The embedding image to MMS is first formatted as a long string rather then a 2D image. For each value of the message image, a PN sequence is generated using an independent seed. These seeds could either be stored, or themselves generated through PN methods. The summation
of all of these PN sequences represents the message image, which is then scaled and added to the cover image of MMS.
To detect the hidden message, each seed is used to generate its PN sequence, which is then correlated with the entire image. If the correlation is high, that bit in the stego image is set to “1”, otherwise a “0”.
The process is then repeated for all the values of the stego image. CDMA improves on the robustness of the hidden message significantly, but requires several orders more of calculation.
CDMA Spread Spectrum in Mid-Band DCT Coefficient in Transform Domain: It is generally preferable to hide information in noisy regions and edges of images, rather then in smoother regions. The benefit is two-fold; Degradation in smoother regions of an image is more noticeable to the HVS, and becomes a prime target for lossy compression schemes. Taking these aspects into consideration, working in a frequency domain of some sort becomes very attractive. The DCT is used in common image compression protocols such as JPEG and MPEG. The technique which we are utilizing is to embed a PN sequence W into the middle frequencies of the DCT block. We can modulate a given DCT block x, y using the equation (3).
Where FM denotes the middle band frequencies, k denotes the gain factor and (x,y) the spatial location of an 8 × 8 pixel block in image I, and (u,v) the DCT coefficient in the corresponding 8 × 8 DCT block.
The middle frequency bands are chosen such that they minimize, rather they avoid the most visual important parts of the image (low frequencies) without over-exposing themselves to removal through compression and noise attacks (high frequencies) [7].
For each 8x8 block x, y of the image, the DCT for the block is first calculated. In that block, the middle frequency components FM are added to the PN sequence W, multiplied by a gain factor k. Coefficients in the low and middle frequencies are copied over to the transformed image unaffected. Each block is then inverse-transformed to give us our final stego image IW [7]. The Steganographic procedure can be made somewhat more adaptive by slightly altering the embedding process to the method given in equation (4).
MULTIMEDIA MESSAGING SERVICE
Here, W (u, v) represents a CDMA image to be hiding, a two-dimensional (2-D) pseudorandom pattern, and k denotes the gain factor.
This slight modification scales the strength of the watermarking based on the size of the particular coefficients being used.
Larger k’s can thus be used for coefficients of higher magnitude…in effect strengthening the stego image in regions that can afford it; weakening it in those that cannot [7].
IV. DISCUSSION AND EXPERIMENTAL RESULTS
The proposed algorithm was implemented using MATLAB 7.0. CDMA spread spectrum techniques satisfies most requirements and is especially robust against statistical attacks, since the hidden information is scattered throughout the image, while not changing the statistical properties. Good results were obtainable using small image to be embed in MMS.
Through experimentation, the gain factor k = 2 was arrived at as a good balance between visual quality and stego image robustness.
Based on the results shown in figure-2 (a), (b) & ©, in compare to the traditionally used LSB technique whose results are shown in figure-1 (a), (b) & ©, we can easily conclude that CDMA in the spatial domain easily meets the requirements for “moderate” robustness, provided that the encoding messages are relatively small.
The results shown in figure-2 (a), (b) & © are particularly impressive when we consider that the message image was entirely unrecognizable after the addition of 50% gaussian noise.
Figure-1 shows (a) Original MMS cover image (1024 x 1024) (b) Stego image ,MMS to be send using LSB algorithm © Recovered
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(noise) hidden message after addition of 1% Gaussian Noise and after JPEG Compression with Quality 95.
Along side the technique utilized in the transform domain works perfectly for unaltered images, with good visual quality of the stego image.