1 Image Authentication by Detecting Traces of Demosaicing June 23, 2008 Andrew C. Gallagher 1,2 Tsuhan Chen 1 Carnegie Mellon University 1 Eastman Kodak Company 2

2 The Problem: Authentication Good News: Computer Graphics and Image Manipulation tools are rapidly advancing. Bad News: How can we confirm that an image is authentically captured by a digital camera? Image Credit: Columbia photographic images and photorealistic computer graphics dataset.

3 Computer Graphic vs. Photographic Photo-Realistic Computer Graphics (PRCG) Photographic Images (PIM) Image Credit: Columbia photographic images and photorealistic computer graphics dataset.

4 Local Forgeries Authentic image Locally Modify Content or insert new Content (Photographic or PRCG) Locally Forged Image

5 Goals and Approach Our Goals: Distinguish between Photographic (PIM) and Computer Graphic (PRCG) Find and Localize Forgeries Our Approach: We focus on the image processing differences between digital cameras and computer graphics. We detect local traces of CFA interpolation.

6 Contributions PIM versus PRCG: Hardware specific features vs. image physics or texture features (Ng et al. 2005, Lyu and Farid 2005) Finding the demosaicing parameters is not necessary. (vs. learning with EM as in Popescu and Farid 2005). Excellent (best) performance on a standard test set using interpolation detection. We test with actual JPEG images from digital cameras.

7 Contributions Detecting Local Forgeries: We show CFA detection is useful for accurately localizing suspicious regions. We show results on forgeries created from real digital camera images. The images are available for research.

8 Image Formation Digital Cameras 1 LensSensor Hardware Correction Balance + Tone Render Sharpen + Noise Cleaning JPEG A/D Computer Graphic Systems Scene Model Balance + Tone Render Sharpen + Noise Cleaning JPEG Virtual Camera Lens

9 CFA Interpolation Digital Cameras Use Color Filter Arrays Interpolation is required In general, missing pixels are a linear combination of neighbors Interpolation can be detected (Gallagher 2000, Popescu and Farid 2005). G 1,1 R 1,2 G 1,3 R 1,4 G 1,5 R 1,6 B 2,1 G 2,2 B 2,3 G 2,4 B 2,5 G 2,6 G 3,1 R 3,2 G 3,3 R 3,4 G 3,5 R 3,6 B 4,1 G 4,2 B 4,3 G 4,4 B 4,5 G 4,6 G 5,1 R 5,2 G 5,3 R 5,4 G 5,5 R 5,6 B 6,1 G 6,2 B 6,3 G 6,4 B 6,5 G 6,6 CFA Interpolation

10 Detecting Traces of CFA Interpolation CFA Traces survive camera processing (even compression) Peak Strength: Apply Filter Estimate Variance Canon EOS JPEG Detect Peak Strength Spatial Domain Peak Frequency Domain  0 2 

11 PRCG versus PIM PIM. Distinct Peak at  =  PRCG. No Distinct Peak at  = 

12 Results: PRCG vs. PIM Columbia Image Set: 800 PIM Digital Camera Images (JPEGs) 800 PRCG Photorealistic Computer Graphic Previous Approaches: Texture statistics (wavelets): Lyu and Farid (2005) Geometric and Physical Features: Ng et al. (2005) Our Feature: Peak Strength

13 Results: PRCG vs. PIM Ng et al. Performance as a function of region size

14 Results: PRCG vs. PIM JPEG Quality Factor Quality Factor 99

15 Results: PRCG vs. PIM JPEG Quality Factor Quality Factor 20

16 Results: PRCG vs. PIM Classification Errors PIM misclassified as PRCG PRCG misclassified as PIM

17 Detecting Local Forgeries Peak is computed locally (64x256) Forged regions usually won’t have CFA traces. Suspicious regions have low. Apply Filter Estimate Variance Canon EOS JPEG Detect Peak Strength Spatial Domain Peak Frequency Domain  0 2 

18 Localizing Forgeries AuthenticForged Analysis Suspicious Regions Good results on all three images. Images are Available at:

19 Discussion CFA traces are destroyed by resizing CFA interpolation could be forged by a sophisticated forger. Many tests will likely be necessary to detect forgeries.

20 Conclusions We propose an elegant CFA interpolation detection for: Distinguishing PIM from PRCG Localizing forged image regions Recovering the CFA parameters is not necessary. Our results are the best yet on a standard image set.