Camera Model I dentification B ased on the Characteristics of CFA and Interpolation

1. Camera Model Identification Based on the Characteristics of CFA and Interpolation Shang Gao1, Guanshuo Xu2, Rui-Min Hu1,*email.nancy.g@gmail.com, gx3@njit.edu, hrm1964@public.wh.hb.cn 1. Wuhan University, China; 2. New Jersey Institute of Technology, US

2. Table of Contents Introduction – Camera Model Identification Color filter Array (CFA) and Color Interpolation in Camera Camera Model Detection Artifacts brought by CFA and Interpolation (Idea of feature extraction) Feature Set 1 Feature Set 2 Feature Set 3 Feature Extraction Flow Diagram Experiment Conclusion

3. Introduction – Camera Model Identification • One category of digital image forensics • If the image we are interested in are captured by a digital camera, then, what is the make and model of the camera?

4. CFA and Interpolation in Camera (1/2) • Fig.2. CFA pattern Images from Internet • Fig.1. CFA and interpolation processing

5. CFA and Interpolation in Camera (2/2) The process of interpolation is to estimates lack color component by its existing neighbor color components. It can be regarded as a weighted average processing and is similar as low pass filtering [8, 9, 16]. Theoretically, it leads to the fact that interpolated color components are ‘smoother’ than the original part statistically. [8] H. Cao and A. C. Kot: Accurate detection of demosaicing regularity for digital image forensics. IEEE Transactions on Information Forensics and Security 4(4), pp. 899-910 (2009) [9] Matthias Kirchner: Efficient Estimation of CFA Pattern Configuration in Digital Camera Images. Media Forensics and Security II, Proc. SPIE, Vol. 754110 (2010) [16] A.E Dirik, N. Memon: Image tamper detection based on demosaicing artifacts. In: ICIP(09), Cairo, Egypt 0 0.25 0 0.25 1 0.25 0 0.25 0 • Fig.3. bilinear interpolation kernel (Green channel)

6. Artifacts brought by CFA and Interpolation We use equation proposed in [16] Consideration We use this feature for camera model identification and expand the feature according to different CFA patterns. There are two pixel sets after interpolation: raw and estimated pixel set. The ‘smooth’ effect by interpolation will lead to different variances between these two sets. The statistical difference between raw pixels values and estimated pixel values is hard to observe using pixel values directly, but it could be more obvious by calculating the statistics of noise part. (1) • [16] A.E Dirik, N. Memon: Image tamper detection based on demosaicing artifacts. In: ICIP(09), Cairo, Egypt • *Noise residual is found by calculating difference between original image and denosed image. • *J. Lukas, J. Fridrich, and M. Goljan, "Digital Camera Identification from Sensor Noise," IEEE Transactions on Information Security and Forensics, vol. 1, no. 2, pp. 205-214, 2006. • Rudin, L., Osher, S., and Fatemi, E.: Nonlinear total variation based noise removal algorithms, physica D., 60 (1992), 259-268.

7. CFA patterns we considered Fig. 4. (a) Bayer Pattern [15]; (b) modified Bayer Pattern [15]; (c) Diagonal Strip Pattern [15]; (d) Vertical Striped Pattern [15]. [15] R. Lukac and K. N. Plataniotis: Color filter arrays: Design and performance analysis. IEEE Transactions on Consumer Electronics, vol. 51, pp. 1260–1267 (2005)

8. What contributes to the classification ability of this feature? Sensors of different camera models may have different CFA patterns. Interpolation algorithm varies among different camera models. Although some of the CFA patterns are not common in digital cameras, grouping pixels periodically and find the statistical difference could be able to capture artifacts during the whole image processing pipeline.

9. Camera Model Detection——Feature set 1: Design (1/2) 33 cases are designed CFA types: Bayer, modified Bayer, diagonal strip, vertical striped pattern Color channel: Green, Red and denote raw and estimated noise under the ith possible sample array Total 36 cases, but 4 cases are same situation for our statistics (Green channel under Bayer and modified Bayer pattern), Final 33 cases (2)

10. Camera Model Detection——Feature set 2 (3) (4) (5) (6) (7) • Inter- color channel interpolation • A popular color difference interpolation scheme [19] utilizes inter-channel correlation between colors to do interpolation. If we assume that the full G is available by some interpolation process, R can be recovered by equation (3). It also can be written as equation (4). • Feature design • The difference of inter-color is ‘smoothed’, to capture these artifacts, feature 2 is designed as equation (5-7) • [19] John S. Ho, Oscar C. Au, Jiantao Zhou, and YuanfangGuo: INTER-CHANNEL DEMOSAICKING TRACES FOR DIGITAL IMAGE FORENSICS. ICME2010

11. Camera Model Detection—— Feature set 3 (8) • Design • For better identical issue, based on the first feature set, we replace the variance statistics by kurtosis statistics and get 33 features as our third feature set. For the 33 possible sample arrays, kurtosis can reflect minor difference of interpolation. Hence, feature set 3 can be calculated as equation (8).

12. Camera Model Detection—— Feature Extraction Flow Diagram Fig. 5. feature extraction flow diagram

13. Experiment: Database (1/5) • ‘Dresden Image Database’ • a public database designed for benchmarking algorithms in the area of digital image forensics. • Setting • Most under same or similar acquisition procedure, such as at the same scenes, same taken positions, and same up to two motives with tripods in Dresden, and photographed with each camera of one set with systematically varying camera setting (flash, focal length and interchanging lens, if possible) [21]. • For source device detection • A collection of same or similar scene images taken by different camera, which can be categorized to do manufactory, model, or device detection or etc.

14. Experiment: Database (2/5) Table.1. Camera model

15. Experiment: Method (3/5) Fig. 6. blocking position • Image Blocking • four 512x512 sub-blocks from center of each JPEG image (no compression after blocking) • Feature extraction • 69-D features are extracted from each sub-block • Model classification • 90% of the images are randomly chosen for training the classifier and the rest of them are used for testing (The random choosing is controlled to make sure the sub-blocks in training part and testing part are not from same image).

16. Experiment: Result (4/5) • Average detection rate under 20 times Table.2. camera model detection accuracy

17. Experiment: Result (5/5) The average detection accuracy of our proposed method is 99.32%, For 324-D Markov feature [20], the average detection accuracy is 98.78%. Fig. 7. average detection accuracy [20] Guanshuo Xu, Shang Gao, Yun Qing Shi, RuiMinHu and Wei Su: Camera-model identification using Markovian transition probability matrix. (IWDW09)

18. Conclusion The artifacts introduced by CFA and interpolation can be considered as differences between models. Three feature sets are designed to catch the artifacts. Combining them together, 69-D features are obtained to do model detection. We use images from seven models of the Dresden image database as our sample resource. The experiment result shows that the detection accuracy of our proposed method works well on seven camera models. The average detection accuracy is 99.32%.

19. Qestions?