CHAPTER 8. Conclusions and Future Work

Transcription

1 CHAPTER 8 Conclusions and Future Work The growth of modern communication needs a special means of security especially on computer network. As there appears a risk that the sensitive information transmitted might be intercepted or distorted by unintended observers for the openness of the internet. So it has resulted in an explosive growth in secure communication and information hiding. Moreover, the information hiding technique can be used extensively in applications like business, military, commercials, anti-criminal, digital forensic and so on. Steganography is the technique of secret communication which has received much attention. In this thesis image based steganography methods have been proposed to increase the performance of the data hiding techniques. This thesis focuses on the analysis and development of image steganography techniques that can hide data with a low detection rate and high payload. 8.1 Summary of contributions The main contribution of this thesis is providing an enhanced image based steganographic technique for achieving the goal of data hiding using steganography. To achieve this goal the various existing image based steganographic techniques i.e. spatial domain based and frequency domain based with an application to data hiding have been investigated. Some of the methods related to such domain available in literature are discussed in chapter 2. All digital file formats can be used for steganography, but the formats those are with a high degree of redundancy are more suitable. The redundant bits of an object are those bits that can be altered without the alteration being detected easily. Chapter 3 presents a study of the different file formats that can be used in steganography. The most popular cover objects used for steganography are digital images. Digital images often have a large amount of redundant data, and this is what steganography uses to hide the message. In Chapter 4, the techniques related to image based steganography on both spatial domain and 155

2 Conclusions and Future Work frequency domain are investigated to understand the details of the basic working process. Along with it, the attacks and techniques related to image steganalysis are discussed. The basic evaluation measures of the image based steganography are also discussed that are used to examine the performance of a steganographic technique. Related to all of the above facts, the existing steganographic algorithms are expanded by combining them with the cryptographic process. In the first case the cryptography and DCT based steganography is combined to form a process that holds the features of steganographic and cryptography technique to increase the security of the secret data as stated in Chapter 5. The secret message is first encrypted using substitution cipher method. Then the cover-image which is to hold the secret data is preprocessed to reduce noise present in the cover-image and increase the dependence between neighboring pixels, so that the embedding process may better utilize the bits. The encrypted message is then embedded in the DCT coefficients of high frequencies of the cover-image. In the process of embedding a modified standard quantization table is used by putting ones (1s) in the coefficients located in the high frequency part. The process of extracting the secret message from the resulting stego-image is also stated. In the experimental analysis, a comparative study of the proposed method with Jpeg Jsteg (Hsu and Wu, 1999) and Chang et al., 2002 based steganography is also conducted. The hiding capacity of the proposed method is more than Jpeg Jsteg and Chang et al., 2002 and also shows better PSNR values than both the methods. Along with this, results of the various other evaluation parameters are also discussed. In Chapter 6, the existing LSB based steganography is also combined with encryption technique to enhance the embedding capacity of image steganography. The secret message undergoes double encryption firstly using transposition cipher method and then with substitution method before embedding into the cover-image file. Then encrypted message is embedded into an image by using least-significant-bit (LSB) technique that enables high capacity of data embedding. Once all the message characters are embedded into the cover-image, the target character is inserted in the pixel of the cover-image immediately next to the one containing the last input character of the message. The process of extraction of secret message is also stated. The main security lies in the encryption method where the secret message that is to be embedded goes though double encryption process and the encryption process is 156

3 Conclusions and Future Work controlled by two different keys. In examining the performance of the proposed steganographic technique, an evaluation scheme for steganographic system is conducted using various performance parameters on various images. A comparative study is also conducted with OPAP (Chan and Cheng, 2004) based on embedding bits of secret message in the cover images. From both the experiments it is found that the proposed method shows better PSNR than OPAP. From other experiments it is found that the proposed method shows better PSNR. Various evaluation measures are also performed to test the proposed method. Chapter 7 introduces an approach of least significant bit (LSB) based steganography in digital images that can override some statistical and structural measures of detection by spreading message bits randomly in which the secret messages are embedded only in the red plane of the cover-image s pixel determined by a pseudorandom number generator (PRNG) initiated by a stego-key. In the process of LSB embedding process, the random number generator selects the hiding points in the pixel s red plane of the cover image by using a random interval method. The random interval produces a random sequence of locations of the secret data. In this method the red plane is selected for data embedding while the blue and green planes are left unmodified. So after performing the embedding operation the unmodified green and blue planes are added to the modified red plane to form the final stego-image. In extraction of the secret message, only the pixel s red plane is selected using the same stego-key that produces the same sequence of random locations of the secret bits. In the experimental analysis, secret message of fixed and variable sizes using different stego-keys are used to embed into the cover-images for examining the effect of increasing key values and message size. A comparison of the proposed method is also done with Amirtharajan et al scheme using PSNR and MSE as performance measure. It is seen that the PSNR value dynamically changes with the change in key value in Amirtharajan et al. scheme whereas in the proposed method the PSNR value are mostly static as the PSNR value are all same for different stego-keys. The method is also evaluated using histogram analysis, visualizing LSB bit-plane and various other performance measures to examine its ability to withstand from attacks. In this thesis, the focus is not only on the embedding strategy, but also on the preprocessing stages, such as secret message encryption and embedding area selection to 157

4 Conclusions and Future Work improve the security. A comparative study of the three proposed method is presented in table 8.1 based on certain criteria which are used in the process of their development. Table 8.1(a): Comparative study of the proposed image steganography methods Criteria Basic operation Operates in the Image domain Image compression used Pre-processing of cover-image Data embedding process Key used Embeds secret data in Encryption of secret data DCT with encryption LSB with encryption Random LSB Encrypts the secret Encrypts the secret Embedding operates message and preprocess message using two only in the red plane the cover- encryption technique of the cover-image s image before and then embeds pixel by modifying its embedding data in data in the LSB of LSB determined by a the high frequency the cover-image pseudorandom coefficients of DCT number generator Frequency domain Spatial domain Spatial domain Lossy Lossless Lossless Yes No No Sequential Sequential Scattering(random) Key is used only in Key is used only in Stego-key is used for the encryption the encryption random pixel process process selection in the coverimage High frequency Red, green and blue Only in red plane of coefficients of the plane of the coverimage the pre-selected pixel cover-image of the cover-image Yes Yes No 158

5 Conclusions and Future Work Table 8.1(b): Comparative study of the proposed image steganography methods Criteria DCT with LSB with Random LSB encryption encryption Robustness against Good Good Excellent attacks Invisibility of Secret Highly invisible Highly invisible Highly invisible data Capacity Average in High High comparison to other two methods Stego-image size Lower than coverimage Same as cover-image Same as cover-image Number of secret key used One key in encryption Two keys, one for transposition and substitution One stego-key is used for random selection of pixels encryption Extraction process depends on Embedding and encryption process Embedding and encryption process Embedding process and the stego-key PSNR value Average High High Overall Security Secure Secure Highly secure Distribution of secret data on the coverimage It embeds secret data in order. It embeds secret data in order Spreads throughout the cover-image Average PSNR value 42 db 57 db 62 db for capacity of 28 K.B Time Computation More Less Less (For embedding and extraction) Key used in extraction Key is used in decryption process Two Keys are used in decryption process Key is used in extracting the pixels consisting of secret data 159

6 Conclusions and Future Work 8.2 Scope for future Work The world of digital media is in a continuous state of evolution. Steganography is regarded as technology that has major competitive applications. While a significant progress in the image steganography techniques has been achieved, still there is scope for the improvement as there is yet to be evolved a standard method and the proposed algorithms can be further enhanced. In this thesis, the study and analysis related to the image based steganography relating to LSB and DCT has been done. The enhancements can be done by using soft computing techniques such as Neural based steganography, Fuzzy and Genetic algorithms based approaches. The future work can also take into considerations of the Quantum computation approaches which can extend the classical steganography for performance enhancement of the existing techniques. The existing transform and spatial domain based approaches can be enhanced with certain variations. The DCT and DWT techniques can also be enhanced by using randomization approach where the secret bits can be embedded randomly selected blocks. Additionally, improving the embedding capacity of these methods that can withstand severe compression can be considered. In the spatial domain enhancement can also be done. The LSB based random embedding where the secret data is embedded only in red plane can be enhanced using two planes for embedding (Red, Green or blue plane) that will increase the embedding capacity and will also preserve the security. The embedding capacity can also be enhanced by using more LSBs and maintaining the statistical properties of the images. 160

7 References Swathi, A. and Jilani, D. S. (2012). Video Steganography by LSB Substitution Using Different Polynomial Equations. International Journal of Computational Engineering Research, ISSN , Vol. 2, Issue 5, pp Tan, K. T.; Ghanbari, M. and Pearson, D. E. (1998). An objective measurement tool for MPEG video quality. Signal Processing, Elsevier, Vol. 70, No. 3, pp Tomar, G. (2012). Effect of Noise on image steganography based on LSB insertion and RSA encryption. IOSR Journal of Engineering, Vol. 2, No. 3, pp Tseng, H. W. and Chang, C. C. (2004). High capacity data hiding in JPEGcompressed images. Institute of Mathematics and Informatics, Vilnius, INFORMATICA, Vol. 15, No. 1, pp Tyagi, V.; Kumar, A.; Patel, R.; Tyagi, S. and Gangwar, S. S. (2012). Image Steganography using Least Significant Bit with Cryptography. Journal of global research in computer science, Vol. 3, No. 3, pp Upham, D. (1997). Jpeg Jsteg, Vassaux, B.; Nguyen, P.; Baudry, S.; Bas, P. and Chassery, J. M. (2002). Scrambling technique for video object watermarking resisting to MPEG-4. In Video/Image Processing and Multimedia Communications 4th EURASIP-IEEE Region 8 International Symposium on VIPromCom (pp ). IEEE. Venkatraman, S.; Abraham, A. and Paprzycki, M. (2004). Significance of Steganography on Data Security. In Information Technology: Coding and Computing, Proceedings. ITCC International Conference on (Vol. 2, pp ). IEEE. Wang, H. and Wang, S. (2004). Cyber warfare: Steganography vs. Steganalysis. Communications of the ACM, Vol. 47, No.10, pp Wang, X.; Yao, Z. and Li, C. T. (2005). A palette-based image steganographic method using colour quantisation. In Image Processing, ICIP IEEE International Conference on (Vol. 2, pp ). IEEE. 180

8 References Wang, Z.; Bovik, A. C. and Lu, L. (2002a). Why is image quality assessment so difficult?. In Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on (Vol. 4, pp ). IEEE. Wang, Z.; Sheikh, H. R. and Bovik, A. C. (2002b) No-reference perceptual quality assessment of JPEG compressed images. In Image Processing Proceedings International Conference on (Vol. 1). IEEE. Watson, A. B. (1994a). Image compression using the discrete cosine transform. Mathematica Journal, Vol. 4, No. 1, pp Watson, A. B. (1994b). Perceptual optimization of DCT color quantization matrices. In Image Processing, Proceedings. ICIP-94., IEEE International Conference (Vol. 1, pp ). IEEE. Wayner, P. (2009). Disappearing cryptography: information hiding: steganography and watermarking. Morgan Kaufmann. Weng, S.; Zhao, Y.; Ni, R. and Pan, J. S. (2009). Lossless data hiding based on prediction-error adjustment. Science in China Series F: Information Sciences, Springer Vol. 52, No. 2, pp Westfeld, A. and Pfitzmann, A. (2000). Attacks on steganographic systems. In Information Hiding (pp ). Springer Berlin Heidelberg. Wu, D. C. and Tsai, W. H. (2003). A Steganographic method for images by pixelvalue differencing. Pattern Recognition Letters, Elsevier, Vol. 24, No. 9, pp Wu, H. R. and Rao, K. R. (Eds.). (2005). Digital video image quality and perceptual coding. CRC press. Wu, M. Y.; Ho, Y. K. and Lee, J. H. (2004). An iterative method of palette-based image Steganography. Pattern Recognition Letters, Elsevier, Vol. 25, No. 3, pp

9 References Wu, N. I. and Hwang, M. S. (2007). Data Hiding: Current Status and Key Issues. International Journal of Network Security, Vol. 4, No. 1, pp.1-9. Yu, Y. H.; Chang, C. C. and Lin, I. C. (2007). A new steganographic method for color and grayscale image hiding. Computer Vision and Image Understanding, Vol. 107, No. 3, pp Zhang, T.; Zhang, Y.; Ping, X. and Song, M. (2006). Detection of LSB Steganography based on Image Smoothness. In Multimedia and Expo, 2006 IEEE International Conference on (pp ). IEEE. Zhang, W.; Zhang, X. and Wang, S. (2007). A double layered plus-minus one data embedding scheme. Signal Processing Letters, IEEE, Vol. 14, No. 11, pp

10 Appendix A Definition of Terms Artifact: Artifacts are the irregularities that may be present in an image after processing. They are not related to the details of the image and sometimes accompany transmitted signals. Bit-plane: The bit-plane of an image is a set of bits corresponding to a given bit position in each of the binary numbers representing the image. Carrier: Carriers are the digital media that are used as a medium to carry the secret data. It is the medium into which the secret data is hidden. Ciphertext: It is the translated text which is formed by encrypting the secret data. It is the unordered or substituted text created by changing its readability and meaning. Coefficient: Coefficients are formed when a signal is transformed a from an image representation into a frequency representation by using discrete transformation. Cover-image: The image which is taken as a medium of covering the secret data. It is the original image into which the secret data is inserted into its redundant bits. Cryptanalysis: It is the study of attacks on the cryptography to find weaknesses in them, without necessarily knowing the key or the algorithm. Decibel: The decibel (db) is a logarithmic unit used to express the ratio between two values of a physical quantity. It is used to measure signal level after processing and is widely used in electronics, signals and communication. Decryption: Decryption is the process of taking encoded or encrypted text and converting it back into original plaintext which are understandable. Eavesdropper: Eavesdroppers are the attackers (unauthorized person) who try to break a signal through communication channel to check if the signal contains any secret data. 183

11 Appendix A Encryption: The process of transformation from plaintext (secret message) to ciphertext (unreadable format) to create a data that is not understandable. Entropy coding: Entropy coding encodes an image by rounding the coefficient values to integer to reduce the size. Entropy coding is lossless compression. Fidelity: Fidelity is the perceptual similarity between images before and after processing. Keystream generator: It generates random sequence of numbers by partitioning the stego-key into random sequences. These sequences allocate positions of the secret bits. MSE: Mean squared error (MSE) shows variation between the cover- image and resultant (stego) image. A high quality image should have less MSE value. Payload: In steganography, payload relates to the amount of secret data that can be hidden into digital media. Pixel indices: Pixel indices are the random locations of the pixels which are formed using random number generator. Plaintext: It is the original secret message that is transformed into unordered state using encryption process. PRNG: The pseudorandom number generator (PRNG) generates sequence of random numbers using a stego-key which is used to select random pixels of the coverimage for hiding the secret data. The key is used as seed for the random number generation. PSNR: The Peak-Signal-to-Noise Ratio (PSNR) is the performance measurement criteria that show the relationship between the bit- or detection-error of two similar signals. A high quality image should have higher PSNR value. Randomization: It is the process of scattering the secret bits in different pixel position of the cover-image that makes the secret data to be in random order. Seed: The seed defines the starting point of a random number generator. It initiates the process of random number generation. 184

12 Appendix A Steganalysis: Steganalysis is the technique to detect whether a given digital media contains hidden data. The steganalysis plays a role in the selection of features or properties of the digital media to test for suspicious data. Steganalyst: Steganalyst is the individual (attacker) who performs the steganalysis with the purpose of detecting suspicious or secret data into a medium when transmitted. Stego-image: The resultant image formed as a result of the steganography algorithm which contains secret data embedded into it. Stego-key: The secret key used in the steganographic method to choose the random pixel position in the image. The security of a stego-key Target character: It is the last character that is embedded into the cover-image. The target character terminates the embedding and extraction process. Zigzag: Zigzag order is performed to group similar frequencies together by sorting the coefficients in zigzag ordering. 185

13 Appendix B List of Publications Journals 1. Laskar, S.A. and Hemachandran, K. (2012). An Analysis of Steganography and Steganalysis Techniques, Assam University Journal of Science and Technology, ISSN: , Vol. 9, No. 2, pp Laskar, S.A. and Hemachandran, K. (2012). High Capacity data hiding using LSB Steganography and Encryption. International Journal of Database Management Systems (IJDMS), ISSN: , Vol. 4, No. 6, pp Laskar, S.A. and Hemachandran, K. (2013). Steganography Based on Random Pixel Selection for Efficient Data Hiding. International journal of Computer Engineering and Technology (IJCET), ISSN: , Vol. 4, Issue 2, pp Laskar, S.A. and Hemachandran, K. (2014). A Review on Image Steganalysis techniques for attacking Steganography, International Journal of Engineering Research & Technology (IJERT), ISSN: , Vol. 3, Issue 1, pp Book Chapters 1. Laskar, S.A. and Hemachandran, K. Combining JPEG Steganography and Substitution Encryption for Secure Data Communication. In David C. Wyld, et al. (Eds). Computer Science & Information Technology (CS & IT): ISSN: , CCSEA, SEA, CLOUD, DKMP, CS & IT 05, pp , (2012). 186

14 Appendix B Conference and Symposium 1. Laskar, S.A. and Hemachandran, K. Combining JPEG Steganography and Substitution Encryption for Secure Data Communication has been presented in Second International Conference on Computer Science, Engineering and Applications (CCSEA)-2012, CCSEA, SEA, CLOUD, DKMP, CS & IT 5, Organized by AIRCC, May, 26-27, 2012, Delhi, India. Citation Count - (a) Laskar, S.A. and Hemachandran, K. (2012). An Analysis of Steganography and Steganalysis Techniques, Assam University Journal of Science and Technology, ISSN: , Vol.9, No-2, pp Cited by 1 Cited by 1. Maan, V. K. and Dhaliwal, H. S. (2013). Vector Quantization in Image Steganography. International Journal of Engineering Research & Technology (IJERT) Vol. 2, No. 4 (2013), pp (b) Laskar, S.A. and Hemachandran, K. (2012). High Capacity data hiding using LSB Steganography and Encryption." International Journal of Database Management Systems (IJDMS), ISSN: , Vol.4, No. 6, pp , DOI: /ijdms Cited by 5 Cited by 1. Bansal, T. and Lamba, R. (2013). Steganography on Colour Images using 32x32 Quantization Table. International Journal for Advance Research in Engineering and Technology (IJARET), Volume 1, Issue V, 2013, pp , ISSN: Vasudev, P. and Saurabh, K. (2013). Video Steganography Based on Improved DCT 32*32 Vector Quantization Method. International Journal of Software & Hardware Research in Engineering (IJSHRE), ISSN: , Vol. 1, No. 4, 2013, pp

15 Appendix B 3. Garg, T. and Vatta, S. (2014). A Review on Data Compression using Steganography. International Journal of Computer Science and Mobile Computing (IJCSMC), Vol.3 Issue.6, June- 2014, pg , ISSN X. (c) Laskar, S.A. and Hemachandran, K. (2013). Steganography Based On Random Pixel Selection for Efficient Data Hiding, International journal of Computer Engineering and Technology (IJCET), ISSN , Vol. 4, Issue 2, pp Cited by 4 Cited by 1. Qasim, M. A. and Pawar, D. (2013). Encryption & Steganography in IPv6 source address International Journal of Computer Engineering & Technology (IJCET), ISSN: , Vol.4, No. 2 (2013) pp Sumathi, C. P.; Santanam, T. and Umamaheswari, G. (2013). A Study of Various Steganographic Techniques Used for Information Hiding." International Journal of Computer Science & Engineering Survey (IJCSES), ISSN: , Vol.4, no. 6 (2013), pp Abdulhameed, Z. N. and Mahmood, M. K. (2014). High Capacity Steganography Based on Chaos and Contourlet Transform for Hiding Multimedia Data. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN: , Volume 5, Issue 1, (2014), pp Mala, R. and Manimozi, I. A Novel Approach for Reversible Data Hiding in Encrypted Images Using Key Based Pixel Selection. International Journal of Computer Science & Engineering Technology (IJCSET), ISSN: , Vol. 5 No. 06 Jun 2014, pp

16 Appendix C Participation in Conferences and Workshops 1. National conference on Current Trends in computer Science (CTCS 2010), Department of Computer Science, Assam University, Silchar 22-24, February 2010, AUS. 2. ISI-AU Workshop On Intelligent Data Analysis: Theory and Application, 1-5, March 2011, Computer Vision and Pattern Recognition Unit, Indian Statistucal Institute, Kolkata and Department of Information Technology, Assam University, Silchar, India. 3. ISI-NEHU Winter School on Soft Computing, Pattern Recognition and Image Processing, October 20 24, 2011, Department of Information Technology, North- Eastern Hill University, Shillong, Meghalaya. 4. Workshop on Application of Mathematics in Computer Science and Engineering, August 2-4, 2011, Department of Mathematics, NIT, Silchar, India. 5. International Conference on Computer Science, Engineering and Applications, Organized by AIRCC, May, 26-27, 2012, New Delhi, India th Workshop on Computational Information Processing, December 3-7, 2012, Electronics and Communication Sciences Unit, ISI Kolkata and Department of Information Technology, Assam University, Silchar, India. 7. Workshop on Role of IPR in Electronics, Communication, Computing and Devices, November 27-28, 2013, Tezpur University IPR Cell and Institute of Engineers, Silchar, India. 189