Complexity Based Sample Selection for Camera Source Identification
Sensor patter noise (SPN) has been proved to be an unique fingerprint of a camera, and widely used for camera source identification. Previous works mostly construct reference SPN by averaging the noise residuals extracted from images like blue sky. However, this is unrealistic in practice and the noise residual would be seriously affected by scene detail, which would significantly influence the performance of camera source identification. To address this problem, a complexity based sample selection method is proposed in this paper. The proposed method is adopted before the extraction of noise residual to select image patches with less scene detail to generate the reference SPN. An extensive comparative experiments show its effectiveness in eliminating the influence of image content and improving the identification accuracy of the existing methods.
KeywordsCamera source identification Sensor pattern noise Sample selection Image complexity
This work is supported by the National Science Foundation of China (No. 61502076) and the Scientific Research Project of Liaoning Provincial Education Department (No. L2015114).
- 2.Dirik, A.E., Sencar, H.T., Memon, N.: Source camera identification based on sensor dust characteristics. In: IEEE Workshop on Signal Processing Applications for Public Security and Forensics, pp. 1–6. IEEE Press, New York (2007)Google Scholar
- 3.Geradts, Z.J., Bijhold, J., Kieft, M., Kurosawa, K., Kuroki, K., Saitoh, N.: Methods for identification of images acquired with digital cameras. In: Enabling Technologies for Law Enforcement, pp. 505–512. SPIE, San Jose (2001)Google Scholar
- 5.Hu, Y., Yu, B., Jian, C.: Source camera identification using large components of sensor pattern noise. In: IEEE International Conference on Computer Science and its Applications, pp. 1–5. IEEE Press, New York (2009)Google Scholar
- 6.Li, R., Li, C.T., Guan, Y.: A compact representation of sensor fingerprint for camera identification and fingerprint matching. In: IEEE International Conference on Acoustics Speech and Signal Processing, pp. 1777–1781. IEEE Press, New York (2015)Google Scholar
- 8.Wu, G., Kang, X., Liu, K.J.R.: A context adaptive predictor of sensor pattern noise for camera source identification. In: 19th IEEE International Conference on Image Processing, pp. 237–240. IEEE Press, New York (2012)Google Scholar
- 9.Li, R., Kotropoulos, C., Li, C.T., Guan, Y.: Random subspace method for source camera identification. In: 25th International Workshop on Machine Learning for Signal Processing, pp. 1–5. IEEE Press, New York (2015)Google Scholar