MP3 Steganalysis Based on Neural Networks

International Journal of Engineering & Technology IJET-IJENS Vol:15 No:04 74 MP3 Steganalysis Based on Neural Networks Ala Alarood, Azizh Abdulmanaf, Subariah Ibrahim, Mohammed J. Alhaddad. Abstract Information secrecy is one of the largest concerns of the researchers these days. Steganography technique used to hide a massage into an object (Text file, Audio file Wave, MP3 or Video). Steganography was improved and developed using computer systems because of multimedia rising in the modern world. In this paper we developed a technique to reveal the MP3 Steganography message using Artificial Neural Network ANN. This technique is used an MP3 file that already has hide a message using LSB. The result was promising. Index Term Steganography, MP3, ANN, LSB, stego file. I. INTRODUCTION Information secrecy is a big concerns hence the ancient world. Different techniques to hide the actual content of the message were raised up depending on the age of human culture and the technical aspects. With computer era the message transfer becomes easier and faster with very high complexity to hide the actual content of the message. The message contents could be hidden using cryptography or either the message itself could be hidden using steganography techniques. Steganography was improved and developed using computer systems because of multimedia rising in the modern word. Image steganography improvement started many years ago with little efficiency. Nowadays, MP3 and audio steganography becomes more and more usable in multimedia transfer over storage media, internet, and social media. The security of MP3 steganography is much larger than it for image steganography, while the same concepts could be implemented for both, such as least significant bit(s) insertion. Mr. Alaa alarood He still continues his Ph.D. in Universiti Teknologi Malaysia (UTM) Faculty of computing. He is Lecturer of king abed alaziz university, Department of Computing and Information Technology.His research interests are Artificial Intelligence ANN, Information Security, and Computer Graphics. (aaarood@kau.edu.sa) Prof. Azizah Abdul Manaf, Professor of Computer Science, Deputy Dean Academic Advanced Informatics School (UTM AIS) Universiti Teknologi Malaysia (UTM) His research interests are Image Processing, Multimedia Security, Computer Forensics, azizaham.kl@utm.my Subariah Ibrahim Associate Professor in Faculty of computing at Universiti Teknologi Malaysia (UTM) His research interests are cryptography, steganography, security of data, pixels, authentication. subariah@utm.my Mohammed J. Alhaddad Associate Professor in Faculty of Information Technology at King Abdul-Aziz University. Honorary Lecturer at Essex University, UK, School of Computer Science and Electronic Engineer and Visiting Associate Professor at University Teknologi Malaysia, His research interests are: network Security, Artificial Intelligence, Robots, Brain Computer Interface BCI (malhaddad@kau.edu.sa). Thus, attacker of MP3 steganography should be designed in more intelligent and deterministic way in comparison with image steganography in order to attack the MP3 stego file. Since the rabidly rise of computer technology and artificial intelligence theorems, strategies, and researches, information and messages handling become a hot area that could be adapted for different fields of applications. In fact the protection and privacy of the information transfer is a major field that the modern technology should solve, lead to continuous developments and researches in both, cryptography and steganography [1]. The secret messaging in ancient world and modern technologies is falling in one of two fields; cryptography and steganography. Cryptography is to hide the message content where the message itself is obvious, but no one can understand its contents unless the specialized personnel. In contrast, the steganography is the art of hiding the message itself, while in such, no one can see the massage, and the media that transfer it should be seen as normal media with no any additions, such as text message. An example of cryptography is the ancient word is the Fibonacci series coding. Where the series should be associated with a specific book. Then, the series will point to specific words in that book in such a way that the word number in the book is pointed by the number in the series. Collecting those numbers will generate the secret message. An example of hybrid cryptography-steganography coding is the cryptex, which is a marble box that contains different block of marble representing a beautiful piece. Arranging the marble blocks represents a secret message that couldn t be seen for the normal people. In addition, if the person knows that, the piece is a cryptex, then, he couldn t understand what message this box includes. In modern years, the computer technology added more complexity and flexibility in data communication. The messages become very easy to be transported. The cryptography were adapted and improved in terms of data encryption and compression, whereas the steganography becomes more common in pictures. The weakness and problems of image based steganography induced the researched in sound files steganography, and may be video files steganography. While in such, the message is being hidden inside image, sound file, or video file respectively [2]. Attack of MP3 stego messages is not an easy task in comparison with the image steganography. This paper implements a complex artificially intelligent way to attack the MP3 file and determine if it contains a stego message or not. II. PROBLEM AND OBJECTIVES The presence of hidden information in the digital word is concerns of protecting the data from attack while transfer over internet or any other digital media (i.e. CD, HDD, DVD, USB-stick... etc.). Steganography is commonly

International Journal of Engineering & Technology IJET-IJENS Vol:15 No:04 75 known as intelligent data transfer security. Hiding the information in a picture and using the digital image for many reasons like their frequently use on the internet for hiding secret information, is a good and cheap way for transfer, it costs no running cost. And also, for secret communications, no direct transfer channel is being used, just, the sender can upload the photo to the internet and the receiver can download it safely. Modern researchers were focusing on developing different techniques of steganography in order to implement and met the requirements of different aspects that could handle the secret message transfer. MP3 steganography was introduces as a higher level secure steganography than the image steganography. In addition, audio file can hold much higher data size than the image steganography. The attack of those steganography methods becomes not easy. The real reliable steganography techniques cooperate very high security level, immunity due to noise, and different transfer media. In fact, hence the MP3 or audio signals has common structure, then, hiding information within it should make a change on that structure. So, there are some methodology in some design can attach that inconsistency of the MP3 stego file. The inconsistency should be generated from the change that must be made on the basic structure of the audio file. So, researches should study that point, analyze different stego files, and get the high impact points that will carry the stego message characteristics with different stego designs and architecture. Those points will be used to attack the MP3 stego files. This paper proposes a modern and efficient technique to achieve security attack of MP3 steganography files based on intelligent artificial neural network design. The developed algorithm should handle MP3 stego files with high security and low distortion. The low distortion MP3 stego files is more robust against attack, because of that, the higher distortion media file is the higher questions marks about its contents, and the ability to process the message contents through the distortion. Neural network implementation will be based upon extracted features from the input MP3 file. Such parameters are studied almost in trails with the neural network design. The extracted features contain the dominant information or features, where those are possible to hold the enough information about steganography processing occlusion within the file. Actually, the structure inconsistency of the stego file is almost non-linear, and sometimes it is time variant structure. So, it s not easy to attach it in one common methodology. The adaptation and design of neural network enable to handle both, time variant and non-linear systems. Thus, the objective of this paper is to detect if an-arbitrary MP3 file contains a steganography message inserted within its structure contents or not. It should be able to distinguish the audio files with very low distortion contents. III. RELATED WORK MP3 steganography was adopted and get rise in the researches when the images becomes weak cover of the stego data. Several methods exist to utilize the concept of Steganography as well as plenty algorithms have been proposed in this regard, especial when working in images. To gather knowledge in this particular research field, we have concentrated on some techniques and methods which are described below. S.K.Bandyopadhyay, Debnath Bhattacharyya proposed introduced the most common techniques in steganography, least significant bit (LSB) insertion. This is a common approach and simple [3]. That approach embeds information in a carrier data. For images as a covering media, the LSB of a pixel is replaced with an M s bit. When working with 24-bit images as carrier, 3-bits can be stored in each pixel by modifying the LSBs of R, G and B array. The resulting image looks identical to the original image. The LSB insertion also adopted to be done in audio cover of the stego data. This method is the oldest computerized steganography method and the most common. It depends only on replacing one or more bits from least significant part of the digital media data contents. In contrast, it s the simpler method and has many weak points on it. For example, it s direct method that enables any one to search within the least significant bits of any multimedia file with simple methodology. Accumulative computer programs can easily to such tasks. Nameer N. EL-Emam Proposed an algorithm to get more level of security on LSB insertion steganography approach. That modification improves higher level of security [4]. This makes the original LSB insertion more difficult to attach. But it becomes weaker these days during the diffusion and common use of LSB insertion method. The conventional LSB method depending on inserting the message bits directly to the cover multimedia file. This research improves the security of LSB by encrypting the message before inserting it. This make the effort that is needed to attach the stego file multiplied by 3 at least Illustrated many concepts and basics related to the steganography and how to hide data messages. A method for hiding data inside a billboard was presented and described. An online data hiding steganography was presented to hide data in output display of an instrument [5]. The improvement of least significant bits insertion was done here. The insertion of message bits becomes not sequential and non-uniform. This means that, inserting the message will be based on predefined series of locations within the multimedia file. In fact, even though this methodology makes an improvement of security rather than LSB, but it still very weak and limited in size of embedded message. Waffaa S. Ahmed at el. presented audio steganography based on amplitude modulation. It transforms the audio signal to wavelet domain and the secret data are being embedded inside the wavelet coefficients. The amplitude modulation process was used to embed the data within the wavelet coefficients [6]. This process has many drawbacks. The first is that, the secret message is not encoded. So, its security level is not rigid. The second is that, the amplitude modulation causes large distortion to the original signal. This makes the original signal very bad in addition to make it weak for the attacks. The hiding rate is relatively low with respect to the high distortion that is resulted. In addition to that, the processing power that is needed in amplitude modulation is very large and it comprises approximations because of that, the amplitude modulation is analogue modulation process.

International Journal of Engineering & Technology IJET-IJENS Vol:15 No:04 76 B. Geetha vani at el., is a hybrid technique that incorporates the steganography and the cryptography together [7]. The encrypted message is directly inserted within the audio signal that is being transformed to discrete wavelet transformation. In fact, the security that was gotten in this literature paper is resulted from the cryptography and encryption algorithm that has been used. So, there is no real effect for the basic steganography technique that was implemented[8]. The work that is presented in this paper is unique; it used the concepts that was presented in the above literatures, and solve the problem that those were faced. In MP3 steganography, the presented paper shows how to attack the different MP3 stego files by implementation of well-structured neural network and analytical attack system. IV. METHODOLOGY The aim of this paper is to process the MP3 file and determine if it s a steganography file or not. The steganography methodology that was aimed as illustrated in section 1 is a complex formulation that makes the data consistent, thus, there is no simple way to attack it. A statistical analysis could be done to find the inconsistency of the audio file when external heterogeneous data exists. In fact, huge statistical analysis is needed to find a way to attack the file. On the other hand, intelligent computational technique could be used instead of pure mathematical analysis, like neural network. Neural network combines many benefits as intelligent detectors or recognizers. It s non-linear, multi-input and multi-output, can handle time variant and invariant systems, in addition to its simpler mathematical structure, with intelligent machine adaptive machine learning techniques. The Neural network, that we designed is comprises many trail and Erro Process as it shown in figure 1 and consists of two things; the first is the structure of network while the second is the input vector of the network. It works in two modes, training mode, and simulation mode. Training comprises that, historical input samples are entered to the network together with the target outputs. The target output is a historical output that is associated to each historical input. While the historical inputs and target outputs are being entered to the network. The training process builds the internal weight and bias vectors of the network, in general, the design of neural network structure comprises that, the selection of the network structure, including the network type, the number of layers, number of neurons in each layer, training function or algorithm, and the activation function for each layer. Our proposed system start by reading the input file, then exam it, if it is an audio file then the process will carry on, otherwise the system will be halt. All the necessary parameters such as file size, bit-rate, frequency and encoding etc. will be used to calculate the essential statistic information as it can be seen in table 1 and it will be used in the proposed system,. Afterward, the audio file will be converted into binary system to process the neural network processes, as shown in figure 2. TABLE I PARAMETERS OF THE MP3 FILE AS THE INPUT VECTOR OF DEVELOPED THE NEURAL NETWORK Parameter Size The variance of the total MP3 audio file 1 The variance of 2000 samples at start of the MP3 file 1 The variance of 2000 samples at end of the MP3 file 1 The variance of 2000 samples at the middle of the MP3 file 1 The standard deviation of the total MP3 file 1 The standard deviation of 2000 samples at start of the MP3 file 1 The standard deviation of 2000 samples at end of the MP3 file 1 The standard deviation of 2000 samples at middle of the MP3 1 The average of the total of MP3 file samples 1 The average of 2000 samples at start of MP3 file 1 The average of 2000 samples at end of MP3 file 1 The average of 2000 samples at middle of MP3 file 1 The correlation between the 1 and 2 channel of the audio MP3 1 The peak signal to noise ration of the MP3 file 1 The mean square error of the MP3 file 1 The sum square error of the MP3 file 1 Sampling frequency 1 Number of bits 1 Fig. 1. Research methodology that was followed in this paper Table 1 has a vector of 18 elements that will be entered to the proposed system which has three layers; input layer, output layer, and one hidden layer as shown in figure 2. The input layer consists of 50 neurons, and the hidden layer consists of 25 neurons, while the output layer is a single neuron layer. Fig. 2. The implemented neural network

International Journal of Engineering & Technology IJET-IJENS Vol:15 No:04 77 The proposed system were used 100 different MP3 file where used to train our model. The training samples where various and contains the different possibilities proprieties; some of the files where used to be 320Kbit encoding, 96Kbit, moreover, the size of the MP3 file and the hidden text are differ. In addition, the model were capable to exam a different LSB steganography technique, such as 1 bit insertion,2 bit insertion, 3 bit insertion and 4 bit insertion. V. RESULTS & CONCLUSION Different results were obtained from different scenario (different size of MP3 file, different encoding, different size of the text message that to be embedded into MP3 file). After using LSB (1, 2, 3, or 4 bit) Steganography to hide the message into MP3 file, the file will be send by the email system or download it from a public site or transferee it by using CD or USB. The proposed model will detect if this file has a hidden message or not. Table II shown the total of empirical result of these scenarios which comprises two categories of testing files. The first are files contain stego messages and the second pure files without any message inserted within it. Thus, the implemented algorithm accuracy is being tested using non stego files and the results of false files rejection are illustrated in the second column in table II. This result is competitive and achieved high accuracy. On the other hand, the stego MP3 files are tested separately. TABLE II RESULTS OF APPLYING A DETECTION PROGRAM ON TEST PATTERN OF THE STEGO MP3 FILES MP3 Encoding Not stego file detection Stego file detection 8K 98% 98% 24K 97% 98% 48K 99% 97% 96K 97% 97% 128K 97% 98% 192K 98% 99% 320K 98% 98% Fig. 3. Block diagram of the presented system The statistical information from table 1 is concatenated to shape the input vector, and then, it entered to the neural network system. The neural network will start to propagate the input through the network layers and calculate the output value. The output value is 1 if the file is a stego MP3 file and 0 otherwise. Figure 3 shown the diagram of the proposed system Figure 3 illustrates the block diagram of the proposed system. The system initially read the target MP3 file and gets its parameters, to form the input vector. The input vector should be propagated proposed model which will processes and encode to determine if the input MP3 file as stego file or not. The output will be a fraction between 0 and 1 according to the activation function of the output layer (i.e. linear function from 0 to 1). Approximation of the output will be used where the output will be considered to be 0 if it s less than 0.2. In contrast, the output will be considered to be 1 if it s higher than 0.8. This approximation (in other words, digital encoding) will activate the final result. The output 1 indicates that, the input file is detected as a steganography MP3 file, while the output 0 indicates that, the input file is detected as normal MP3 file and no steganography data is assumed to be embedding within it. Some time by transferring the Stego file via social media, such as email, messenger, memory storage, or any other image transfer technology; the most important criterion is that, the original MP3 (without hidden message) will not lose its accuracy. If the stego audio file faced a distortion in an audibly noticeable way, two drawbacks will be faced; the first is to make the audio MP3 not reliable for common use and human ear, and the second is, it will be subjected to some question marks and thus, it will be easy in some way to detect it. The performance evaluation that is described in table II is actually the replacement of comparison with other related work as it shown in table III. It in fact compares the power of the presented algorithm with the impact of the related works to hide the data message within a stego file. TABLE III COMPARSION FOR STEGO FILE AND SUCCESSFUL ACCURACFY Paper Technique Proposed Model Accuracy [9] LSB Conversional 75% [10] AM PCA 95% [11] LSB Embedding SVM 95% with FFt [12] QIM Embedded SVM 89% [13] Mel-Ceptrum Embedded SVM 50% [14] Multiply Embed model Embedded SVM 94% The quality of input stego audio files that is shown in figure 4 illustrates that, the proposal model using neural network can attack the steganography MP3 file. The very low

International Journal of Engineering & Technology IJET-IJENS Vol:15 No:04 78 distortion will not stop the proposal model and its capable detects the message existence regardless of the noise level. Fig. 4. Comparsion of MP3 file before and after Embedding the secrt text message The proposed model has the ability to extract the features of MP3 file, then, it is able to detect the inconsistency in the basic structure. In contrast, the steganography algorithms are normally concentrates on hiding the message with low distortion of the general structure of file with fine details like Peak Signal-to-Noise Ratio PSNR and Mean Square Error MSE. The accuracy of building steganography file with very low distortion is not real performance. That is because; the basic structure of the audio or sound signal is not the same before and after steganography. Therefore, the proposed Model presented in this paper used unique feature set of audio file and shown an ability attack different stego file with an accuracy percentage around 98%. REFERENCES [1] Curran, K. and Bailey, (June, 2006) An evaluation of imagebased steganography methods, International Journal of Digital Evidence, Multimed Tools Appl, Springer Science + Business Media, 30, pp 55-88. [2] Wang, H & Wang, S, (October 2004), Cyber warfare: Steganography vs. Steganalysis, Communications of the ACM, New York, NY, USA, 47(10), pp 76-82 [3] S.K.Bandyopadhyay, Debnath Bhattacharyya, Poulumi Das, S. Mukherjee, D. Ganguly, (August 2008), A Tutorial Review on Steganography, IC3 Noida, pp. 106-114. [4] Nameer N. EL-Emam, (April 2007), Hiding a large amount of data with high security using steganography algorithm, Journal of Computer Science, pp 223 232, [5] Shashikala Channalli, Ajay Jadhav, (2009) "Steganography: An Art of Hiding Data", International Journal on Computer Science and Engineering IJCSE, 1(3), pp 137-141. [6] Waffaa S. Ahmed and Loay E. George, (March, 2013), "Audio Hiding Using Wavelet Transform with Amplitude Modulation", Journal of Al-Nahrain University, 16 (1), pp.183-188. [7] B. Geetha vani, Prof. E. V. Prasad, (2003) "A Hybrid Model for Secure Data Transfer in Audio Signals using HCNN and DD DWT", (IJACSA) International Journal of Advanced Computer Science and Applications IJACSA, 4(7), pp 202-208. [8] Abdulaleem Z. Al-Othmani, Azizah Abdul Manaf and Akram M. Zeki. A Survey on Steganography Techniques in Real Time Audio Signals and Evaluation. IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 1, January 2012. ISSN (Online): 1694-0814. www.ijcsi.org [9] M. Jiang, E. Wong, N. Memon and X. Wu, (December 2004), "A Simple Technique For Estimating Message Lengths For Additive Noise Steganography", 8th International Conference on Control, Automation, Robotics lind Vision Kunming, China, 1(2), pp 983-986. [10] J. W. Fu, Y.C. QI and J. S. Yuan, (Nov 2007), Wavelet Domain Audio Steganalysis Based On Statistical Moments And PCA, Proceedings of the 2007 International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, pp 1619-1623. [11] W. Zeng, H. Ai, R. Hu, (Aug 2007) A Novel Steganalysis Algorithm of Phase Coding in Audio Signal, International Conference on Advanced Language Processing and Web Information Technology, 1(4), pp 261-264. [12] H.-Y. Gao, A Blind Steganalysis Algorithm of QIM-based Hiding, (Aug 2008), International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 273 276. [13] J.-W. Fu, Y.-C. QI, J.-S. Yuan, (July 2009), Wavelet Domain Audio Steganalysis For Multiplicative Embedding Model, International Conference On Wavelet Analysis And Pattern Recognition, Baoding, pp. 429 432. [14] J. Cosic and M. Baca, (Sep 2010), Steganography and Steganalysis - Does Local Web Sites Contain Stego Contents?, ELMAR, 2010 PROCEEDINGS, pp 85-88. Mr. Alaa alarood He still continues his Ph.D. in Universiti Teknologi Malaysia (UTM) Faculty of computing. He is Lecturer of king abed alaziz university, Department of Computing and Information Technology.His research interests are Artificial Intelligence ANN, Information Security, and Computer Graphics. (aaarood@kau.edu.sa) Prof. Azizah Abdul Manaf, Professor of Computer Science, Deputy Dean Academic Advanced Informatics School (UTM AIS) Universiti Teknologi Malaysia (UTM) her research interests are Image Processing, Multimedia Security, Computer Forensics, azizaham.kl@utm.my Subariah Ibrahim Associate Professor in Faculty of computing at Universiti Teknologi Malaysia (UTM) her research interests are cryptography, steganography, security of data, pixels, authentication. subariah@utm.my Mohammed J. Alhaddad Associate Professor in Faculty of Information Technology at King Abdul-Aziz University. Honorary Lecturer at Essex University, UK, School of Computer Science and Electronic Engineer and Visiting Associate Professor at University Teknologi Malaysia, His research interests are: network Security, Artificial Intelligence, Robots, Brain Computer Interface BCI (malhaddad@kau.edu.sa)..