Abstract
Non-intrusive digital image forensics (NIDIF) aims at authenticating the validity of digital images utilizing their intrinsic characteristics when the active forensic methods, such as digital watermarking or digital signatures, fail or are not present. The NIDIF for lossy JPEG compressed images are of special importance due to its pervasively use in many applications. Recently, researchers showed that certain types of tampering manipulations can be revealed when JPEG re-compress artifacts (JRCA) is found in a suspicious JPEG image. Up to now, most existing works mainly focus on the detection of doubly JPEG compressed images without block shifting. However, they cannot identify another JRCA – the shifted double JPEG (SD-JPEG) compression artifacts which are commonly present in composite JPEG images. In this paper, the SD-JPEG artifacts are modeled as a noisy 2-D convolutive mixing model. A symmetry verification based method and a first digit histogram based remedy method are proposed to form an integral identification framework. It can reliably detect the SD-JPEG artifacts when a critical state is not reached. The experimental results have shown the effectiveness of the proposed framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bayram, S., Sencar, H., Memon, N.: Identifying digital cameras using cfa interpolation. In: Advances in Digital Forensics II, vol. 222, pp. 289–299 (2006)
Bell, J.A., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Chang, C.C., Lin, C.J.: LIBSVM:a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chen, M., Fridrich, J., Lukáš, J., Goljan, M.: Imaging Sensor Noise as Digital X-Ray for Revealing Forgeries. In: Furon, T., Cayre, F., Doërr, G., Bas, P. (eds.) IH 2007. LNCS, vol. 4567, pp. 342–358. Springer, Heidelberg (2008)
Fu, D.D., Shi, Y.Q., Su, W.: A generalized benford’s law for jpeg coefficients and its applications in image forensics - art. no. 65051l. In: Security, Steganography, and Watermarking of Multimedia Contents IX, vol. 6505, p. L5051 (2007)
He, J., Lin, Z., Wang, L., Tang, X.: Detecting Doctored JPEG Images Via DCT Coefficient Analysis. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part III. LNCS, vol. 3953, pp. 423–435. Springer, Heidelberg (2006)
Johnson, M.K., Farid, H.: Exposing digital forgeries in complex lighting environments. IEEE Trans. Inf. Forensics Security 2(3), 450–461 (2007)
Li, B., Shi, Y.Q., Huang, J.W.: Detecting doubly compressed jpeg images by using mode based first digit features. In: IEEE Workshop on Multimedia Signal Processing, pp. 730–735 (2008)
Lukas, J., Fridrich, J.: Estimation of primary quantization matrix in double compressed jpeg images. In: Proc. of DFRWS, Cleveland, OH, USA (2003)
Luo, W., Qu, Z., Huang, J., Qiu, G.: A novel method for detecting cropped and recompressed image block. In: IEEE Int. Conf. on Acoustics Speech and Signal Processing, April 15-20, vol. 2, pp. II-217–II-220 (2007)
Ng, T.T., Chang, S.F., Tsui, M.P.: Using geometry invariants for camera response function estimation. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, June 17-22, pp. 1–8 (2007)
Popescu, A.: Statistical Tools for Digital Image Forensics. Ph.D. thesis, Department of Computer Science,Dartmouth College (2005)
Qu, Z., Luo, W., Huang, J.: A convolutive mixing model for shifted double jpeg compression with application to passive image authentication. In: IEEE Int. Conf. on Acoustics Speech and Signal Processing, March 31-April 4, pp. 1661–1664 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qu, Z., Luo, W., Huang, J. (2012). Identifying Shifted Double JPEG Compression Artifacts for Non-intrusive Digital Image Forensics. In: Hu, SM., Martin, R.R. (eds) Computational Visual Media. CVM 2012. Lecture Notes in Computer Science, vol 7633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34263-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-34263-9_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34262-2
Online ISBN: 978-3-642-34263-9
eBook Packages: Computer ScienceComputer Science (R0)