A Novel Robust Image Forensics Algorithm Based on L1-Norm Estimation
To improve the robustness of the typical image forensics with the noise variance, we propose a novel image forensics approach that based on L1-norm estimation. First, we estimate the kurtosis and the noise variance of the high-pass image. Then, we build a minimum error objective function based on L1-norm estimation to compute the kurtosis and the noise variance of overlapping blocks of the image by an iterative solution. Finally, the spliced regions are exposed through K-means cluster analysis. Since the noise variance of adjacent blocks are similar, our approach can accelerate the iterative process by setting the noise variance of the previous block as the initial value of the current block. According to analytics and experiments, our approach can effectively solve the inaccurate locating problem caused by outliers. It also performs better than reference algorithm in locating spliced regions, especially for those with realistic appearances, and improves the robustness effectively.
KeywordsImage splicing L1-norm estimation Noise variance Image forensics
This work was supported by the NSFC under U1536105 and 61303259, National Key Technology R&D Program under 2014BAH41B01, Strategic Priority Research Program of CAS under XDA06030600, and Key Project of Institute of Information Engineering, CAS, under Y5Z0131201.
- 1.Dirik, A.E., Memon, N.D.: Image tamper detection based on demosaicing artifacts. IEEE Trans. Image Process., 1497–1500 (2009)Google Scholar
- 3.Johnson, M.K., Farid, H.: Exposing digital forgeries by detecting inconsistencies in lighting. In: Proceedings of the ACM 7th Workshop on Multimedia and Security, pp. 1–10 (2005)Google Scholar
- 4.Johnson, M, K., Farid, H.: Exposing digital forgeries through specular highlights on the eye. In: International Workshop on Information Hiding, pp. 311–325 (2007)Google Scholar
- 7.Pan, X., Zhang, X., Lyu, S.: Exposing image splicing with inconsistent local noise variances. In: IEEE International Conference on Computational Photography, pp. 1–10 (2012)Google Scholar
- 10.Zoran, D., Weiss, Y.: Scale invariance and noise in natural images. In: IEEE International Conference on Computer Vision, pp. 2209–2216 (2009)Google Scholar
- 11.Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vision 21 (2001)Google Scholar
- 12.Chen, X.: Least absolute linear regression. Appl. Stat. Manage. 5, 48 (1989)Google Scholar
- 13.Li, Z.: Introduction of least absolute deviation method. Bull. Maths 2, 40 (1992)Google Scholar
- 14.Hsu, Y.F., Chang, S.F.: Detecting image splicing using geometry invariants and camera characteristics consistency. In: IEEE International Conference on Multimedia and Expo, pp. 549–552 (2006)Google Scholar