1 / 20

Image Authentication by Detecting Traces of Demosaicing

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. The Problem: Authentication. Good News: Computer Graphics and Image Manipulation tools are rapidly advancing.

wendi
Download Presentation

Image Authentication by Detecting Traces of Demosaicing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Image Authentication by Detecting Traces of Demosaicing June 23, 2008 Andrew C. Gallagher1,2 Tsuhan Chen1 Carnegie Mellon University1 Eastman Kodak Company2 1

  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. 2

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

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

  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. 5

  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. 6

  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. 7

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

  9. CFA Interpolation 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). 9

  10. Apply Filter Peak p 0 2p Spatial Domain Frequency Domain Detecting Traces of CFA Interpolation Canon EOS JPEG EstimateVariance Detect PeakStrength • CFA Traces survive camera processing(even compression) • Peak Strength: 10

  11. PRCG versus PIM PIM. Distinct Peak at w = p PRCG. No Distinct Peak at w = p 11

  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 12

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

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

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

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

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

  18. Localizing Forgeries SuspiciousRegions Authentic Forged Analysis Good results on all three images. 18 Images are Available at: http://amp.ece.cmu.edu/people/Andy/authentication.html

  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. 19

  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. 20

More Related