Exposing Region Splicing Forgeries with Blind Local Noise Estimation
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Region splicing is a simple and common digital image tampering operation, where a chosen region from one image is composited into another image with the aim to modify the original image’s content. In this paper, we describe an effective method to expose region splicing by revealing inconsistencies in local noise levels, based on the fact that images of different origins may have different noise characteristics introduced by the sensors or post-processing steps. The basis of our region splicing detection method is a new blind noise estimation algorithm, which exploits a particular regular property of the kurtosis of nature images in band-pass domains and the relationship between noise characteristics and kurtosis. The estimation of noise statistics is formulated as an optimization problem with closed-form solution, and is further extended to an efficient estimation method of local noise statistics. We demonstrate the efficacy of our blind global and local noise estimation methods on natural images, and evaluate the performances and robustness of the region splicing detection method on forged images.
KeywordsBlind local noise estimation Natural image statistics Digital image forensics
We would like to thank Daniel Zoran, Zhouchen Lin and Babak Mahdian for kindly sharing the images, codes and results of their works with us. We would also like to thank the two anonymous reviewers for their constructive comments that helped us improve this work. This work is supported in part by the National Science Foundation under Grant Nos. IIS-0953373, IIS-1208463 and CCF-1319800.
- Benedict, T. R., & Soong, T. T. (1967). The joint estimation of signal and noise from the sum envelope. IEEE Transactions on Information Theory, 13(3), 447–454.Google Scholar
- Bilcu, R. C., & Vehvilainen, M. (2005). A new method for noise estimation in images. In IEEE EURASIP International workshop on nonlinear signal and image processing.Google Scholar
- Chen, M., Fridrich, J. J., Lukás, J., & Goljan, M. (2007). Imaging sensor noise as digital X-ray for revealing forgeries. In Information hiding (pp. 342–358).Google Scholar
- Chen, W., Shi, Y. Q., & Su, W. (2007). Image splicing detection using 2-D phase congruency and statistical moments of characteristic function. In Society of photo-optical instrumentation engineers (SPIE) conference series (Vol. 6505). doi: 10.1117/12.704321.
- Crow, F. (1984). Summed-area tables for texture mapping. In ACM SIGGRAPH.Google Scholar
- Danielyan, A., & Foi, A. (2009). Noise variance estimation in nonlocal transform domain. In International workshop on local and non-local approximation in image processing.Google Scholar
- Filler, T., Fridrich, J. J., & Goljan, M. (2008). Using sensor pattern noise for camera model identification. In IEEE International conference on image processing. San Diego, CA.Google Scholar
- For̈stner, W. (1998). Image preprocessing for feature extraction in digital intensity, color and range images. In: Springer lecture notes on Earth siences.Google Scholar
- Franzen, R. (1999). Kodak lossless true color image suite. http://r0k.us/graphics/kodak. Accessed 17 Dec 2013.
- Fu, D., Shi, Y. Q., & Su, W. (2007). Image splicing detection using 2-d phase congruency and statistical moments of characteristic function. In Proceedings of SPIE security, steganography, and watermarking of multimedia contents IX.Google Scholar
- He, J., Lin, Z., Wang, L., & Tang., X. (2006). Detecting doctored JPEG images via DCT coefficient analysis. In ECCV.Google Scholar
- Hsu, Y. F., & Chang, S. F. (2006). Detecting image splicing using geometry invariants and camera characteristics consistency. In IEEE International Conference on Multimedia and Expo.Google Scholar
- Hsu, Y. F., & Chang, S. F. (2007). Image splicing detection using camera response function consistency and automatic segmentation. In IEEE International Conference on Multimedia and Expo.Google Scholar
- Lin, Z., Wang, R., Tang, X., & Shum, H. (2005). Detecting doctored images using camera response normality and consistency. In CVPR.Google Scholar
- Liu, C., Szeliski, R., Kang, S. B., Zitnick, C. L., & Freeman, W. T. (2008). Automatic estimation and removal of noise from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 299–314. http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.1176.Google Scholar
- Liu, X., Tanaka, M., Okutomi, M. (2012). Noise level estimation using weak textured patches of a single noisy image. In IEEE International conference on image processing.Google Scholar
- Lukas, J., Fridrich, J., & Goljan, M. (2006). Detecting digital image forgeries using sensor pattern noise. In Proceedings of SPIE security, steganography, and watermarking of multimedia contents VIII.Google Scholar
- Matzner, R., & Engleberger, F. (1994). An SNR estimation algorithm using fourth-order moments. In IEEE International symposium on information theory.Google Scholar
- Nakamura, J. (Ed.). (2006). Image sensors and signal processing for digital still cameras. Boca Raton: Taylor and Francis.Google Scholar
- Ng, T. T., & Chang, S. F. (2004). A model for image splicing. In IEEE International conference on image processing (ICIP). Singapore.Google Scholar
- Pan, X., Zhang, X., & Lyu, S. (2011). Exposing image forgery with blind noise estimation. In The 13th ACM workshop on multimedia and security. Buffalo, NY.Google Scholar
- Pan, X., Zhang, X., & Lyu, S. (2012). Blind local noise estimation for medical images reconstructed from rapid acquisition. In SPIE Symposium on medical imaging. San Diego, CA.Google Scholar
- Pan, X., Zhang, X., & Lyu, S. (2012). Detecting splicing in digital audios using local noise level estimation. In IEEE International conference on acoustics, speech, and signal processing (ICASSP). Kyoto, Japan.Google Scholar
- Pan, X., Zhang, X., & Lyu, S. (2012). Exposing image splicing with inconsistent local noise variances. In IEEE International conference on computational photography. Seattle, WA.Google Scholar
- Pauluzzi, D. R., & Beaulieu, N. C. (2000). A comparison of SNR estimation techniques for the AGWN channel. IEEE Transactions on Communications, 48(10), 1681–1691.Google Scholar
- Ponomarenko, N. N., Lukin, V. V., Abramov, S. K., Egiazarian, K. O., & Astola, J. T. (2003). Blind evaluation of additive noise variance in textured images by nonlinear processing of block DCT coefficients. In SPIE Proceedings (Vol. 5014, pp. 178–189).Google Scholar
- Popescu, A., & Farid, H. (2004). Statistical tools for digital forensics. In 6th International workshop on information hiding. Toronto, Canada.Google Scholar
- Portilla, J. (2004). Full blind denoising through noise covariance estimation using Gaussian scale mixtures in the wavelet domain. In International conference on image processing. doi: 10.1109/ICIP.2004.1419524.
- Qu, Z., Qiu, G., & Huang, J. (2009). Detect digital image splicing with visual cues. In: S. Katzenbeisser, & A. R. Sadeghi (Eds.) International workshop on information hiding (pp. 247–261).Google Scholar
- Rank, K., Lendl, M., & Unbehauen, R. (1999). Estimation of image noise variance. In IEE Proceedings. Vision, Image and Signal Processing (Vol. 146, pp. 80–84).Google Scholar
- Rudin, L., Lions, P., & Osher, S. (2003). Multiplicative denoising and deblurring: Theory and algorithms. In S. Osher & N. Paragios (Eds.), Geometric level set methods in imaging, vision, and graphics. New York: Springer.Google Scholar
- Schaefer, G., & Stich, M. (2004). UCID—an uncompressed colour image database. In Proc. SPIE, storage and retrieval methods and applications for multimedia.Google Scholar
- Schmidt, U., Schelten, K., & Roth, S. (2011). Bayesian deblurring with integrated noise estimation. In IEEE International conference on computer vision. Colorado Springs, CO.Google Scholar
- Sencar, H. T., & Memon, N. (Eds.). (2012). Digital image forensics: There is more to a picture than meets the eye. Dordrecht: Springer.Google Scholar
- Serra, J. (1988). Image analysis and mathematical morphology: Theoretical advances. Image Analysis and Mathematical Morphology. London: Academic Press.Google Scholar
- Shi, Y. Q., Chen, C., & Chen, W. (2007). A natural image model approach to splicing detection. In Proceedings of the 9th workshop on multimedia and security (pp. 51–62). New York, NY: ACM. doi: 10.1145/1288869.1288878.
- Simoncelli, E. P., & Freeman, W. T. (1995). The steerable pyramid: A flexible architecture for multi-scale derivative computation. In IEEE Second international conference on image processing.Google Scholar
- Tai, S. C., & Yang, S. M. (2008). A fast method for image noise estimation using Laplacian operator and adaptive edge detection. In International symposium on communications, control and signal processing.Google Scholar
- van Hateren, J. H., & van der Schaaf, A. (1998). Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society B: Biological Sciences, 265(1394), 359–366.Google Scholar
- Viola, P., & Jones, M. (2002). Robust real-time object detection. International Journal of Computer Vision, 57(2), 137–154.Google Scholar
- Wainwright, M. J., & Simoncelli, E. P. (2000). Scale mixtures of Gaussians and the statistics of natural images. Cambridge, MA: MIT Press. Google Scholar
- Wang, W., Dong, J., & Tan, T. (2009). Effective image splicing detection based on image chroma. In IEEE International conference on image processing.Google Scholar
- Withagen, P., Groen, F., & Schutte, K. (2005). CCD characterization for a range of color cameras. In Instrumentation and measurement technology conference, 2005 (Vol 3, pp. 2232–2235). doi: 10.1109/IMTC.2005.1604573.
- Zoran, D., & Weiss, Y. (2009). Scale invariance and noise in nature image. In IEEE International conference on computer vision. Kyoto, Japan.Google Scholar