Abstract
Aiming at detecting secret information hidden in a given image using steganographic tools, steganalysis has been of interest for years. In particular, universal steganalysis, not limited to attacking a specific steganographic tool, is of extensive interests due to its practicality. Recently, splicing detection, another important area in digital forensics has attracted increasing attention. Is there any relationship between steganalysis and splicing detection? Is it possible to apply universal steganalysis methodologies to splicing detection? In this paper, we address these intact and yet interesting questions. Our analysis and experiments have demonstrated that, on the one hand, steganography and splicing have different goals and strategies, hence, generally causing different statistical artifacts on images. However, on the other hand, both of them make the touched (stego or spliced) image different from the corresponding original (natural) image. Therefore, natural image model based on a set of carefully selected statistical features under the machine learning framework can be used for steganalysis and splicing detection. It is shown in this paper that some successful universal steganalytic schemes can make promising progress in splicing detection if applied properly. A more advanced natural image model developed from these state-of-the-art steganalysis methods is thereafter presented. Furthermore, a concrete implementation of the proposed model is applied to the Columbia Image Splicing Detection Evaluation Dataset, which has achieved an accuracy of 92%, indicating a significant advancement in splicing detection.
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References
Kharrazi, M., Sencar, H.T., Memon, N.: Image Steganography: Concepts and Practice. In: Lecture Note Series, Institute for Mathematical Sciences, National University of Singapore (2004)
Columbia DVMM Research Lab: Columbia Image Splicing Detection Evaluation Dataset (2004), http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSpliced-DataSet/AuthSplicedDataSet.htm
http://www.ee.columbia.edu/dvmm/publications/04/TR_splicingDataSet_ttng.pdf
Lyu, S., Farid, H.: Detecting Hidden Messages Using Higher-order Statistics and Support Vector Machines. In: Information Hiding Workshop, Noordwijkerhout, Netherlands (2002)
Shi, Y.Q., Xuan, G., Zou, D., Gao, J., Yang, C., Zhang, Z., Chai, P., Chen, W., Chen, C.: Steganalysis Based on Moments of Characteristic Functions Using Wavelet Decomposition, Prediction-error Image, and Neural Network. In: International Conference on Multimedia and Expo, Amsterdam, Netherlands (2005)
Zou, D., Shi, Y.Q., Su, W., Xuan, G.: Steganalysis Based on Markov Model of Thresholded Prediction-Error Image. In: International Conference on Multimedia and Expo, Toronto, ON, Canada (2006)
Shi, Y.Q., Chen, C., Chen, W.: A Markov Process Based Approach to Effective Attacking JEPG Steganography. In: Information Hiding Workshop, Old Town Alexandria, VA, USA (2006)
Chen, C., Shi, Y.Q., Xuan, G.: Steganalyzing Texture images. In: International Conference on Image Processing, St. Antonio, TX, USA (2007)
Fridrich, J.: Feature-based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes. In: Information Hiding Workshop, Toronto, ON, Canada (2004)
Ng, T.-T., Chang, S.-F., Sun, Q.: Blind Detection of Photomontage Using Higher Order Statistics. In: IEEE International Symposium on Circuits and Systems, Vancouver, BC, Canada (2004)
Fu, D., Shi, Y.Q., Su, W.: Detection of Image Splicing Based on Hilbert-Huang Transform and Moments of Characteristic Functions with Wavelet Decomposition. In: Shi, Y. Q., Jeon, B. (eds.) Digital Watermarking, Proceeding of 5th International Workshop on Digital Watermarking, Jeju Island, Korea (2006)
Chen, W., Shi, Y.Q., Su, W.: Image Splicing Detection Using 2-D Phase Congruency and Statistical Moments of Characteristic Function. In: Delp, E. J., Wong, P.W. (eds.) Security, Steganography and Watermarking of Multimedia Contents IX, Proceeding. of SPIE, San Jose, CA, USA (2007)
Farid, H.: A Picture Tells a Thousand Lies. New Scientist 179(2411), 38–41 (2003)
Ng, T.-T., Chang, S.-F.: A Model for Image Splicing. In: IEEE International Conference on Image Processing, Singapore (2004)
Bayram, S., Avcibas, I., Sankur, B., Memon, N.: Image Manipulation Detection. Journal of Electronic Imaging 15(4) (2006)
Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/cjlin/libsvm
Fawcett, T.: Roc Graphs: Notes and Practical Considerations for Researchers, http://home.comcast.net/~tom.fawcett/public_html/papers/ROC101.pdf
Leon-Garcia, A.: Probability and Random Processes for Electrical Engineering, 2nd edn. Addison-Wesley Publishing Company, Reading (1994)
Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (1999)
Shi, Y.Q., Sun, H.: Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms, and Standards. CRC Press, Boca Raton (1999)
Forchheimer, R., Kronander, T.: Image Coding from Waveforms to Animation. IEEE Transactions on Acoustics, Speech and Signal Processing 37(12), 2008–2023 (1989)
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Shi, Y.Q., Chen, C., Xuan, G., Su, W. (2008). Steganalysis Versus Splicing Detection. In: Shi, Y.Q., Kim, HJ., Katzenbeisser, S. (eds) Digital Watermarking. IWDW 2007. Lecture Notes in Computer Science, vol 5041. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92238-4_13
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DOI: https://doi.org/10.1007/978-3-540-92238-4_13
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