Exposing Digital Forgeries in Color Array Interpolated Images

1. Exposing Digital Forgeries in Color Array Interpolated Images Presented by: Ariel Hutterer Final Fantasy ,2001 My eye

2. References • Alin C.Popescu and Hany Farid: • Exposing Digital Forgeries in Color Filter Array Interpolated Images. • Yizhen Huang: • Can Digital Forgery Detection Unevadable? A Case Study : Color Filter Array Interpolation Statistical Feature Recovery. • Hagit El Or • Demosaicing.

3. Outline • Introduction • Digital Cameras • Interpolations • Detecting CFA Interpolation • Results • Crack Methods • Computer Graphics

4. Introduction- forgeries • Low cost: cameras ,photo editing software. • Images can be manipulated easily. • Splicing.

5. Introduction- forgeries • Images have a huge impact in public opinion. • Legal world. • Scientific evidence.

6. Introduction - preventing forgeries approaches • Two principal approaches to prevent forgeries: • Digital watermarking: • Means that image can be authenticated. • Drawbacks: • Specially equipped digital cameras ,that insert the watermark. • Assume that watermark cannot be easily removed and reinserted. (but ….it is???) • Statistic analysis: • Most color digital cameras , introduces specific correlation: • A third of the image are captured by a sensor. • Two thirds of the image are interpolated. • Images manipulated must alter this specific statistic.

7. Outline • Introduction • Digital Cameras • Interpolations • Detecting CFA Interpolation • Results • Crack Methods • Computer Graphics

8. Digital Cameras • Most Color digital Cameras have a single monochrome Array of sensors

9. Digital Cameras • How does color form with monochrome sensor for each pixel?

10. Digital Cameras-Bayer Color Array • Half pixels are Green ,quarter are Red and quarter are Blue

11. Digital Cameras-Bayer Color Array • Several possible arranges Diagonal Bayer Bayer Diagonal Striped Psudo-random Bayer

12. Digital cameras - forming color

13. Digital cameras - forming color

14. Digital cameras - forming color Interpolation

15. Digital cameras - forming color • Bayer Array For almost all Digital Cameras • Color Interpolation different for each make of Digital Camera Interpolation

16. Outline • Introduction • Digital Cameras • Interpolations • Detecting CFA Interpolation • Results • Crack Methods • Computer Graphics

17. Interpolations • Naive – per channel interpolation • Nearest neighbor ,Bilinear interpolation • Inter-channel dependencies and correlations – • Reconstruct G channel, then reconstruct R & B based on G. Reconstruct all 3 channels constrained with inter-channel dependence. • Adaptive reconstruction – • Measure local image variations (e.g. edges, gradients, business) and reconstruct accordingly.

18. B B B B B B B B B GGG G G G G GGG G G G G GGG G G G G R R R R R R R R R R R R B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B G G G G G G G G G G G G G GG G G G G G GG G G G G G GG G G G G G GG G G G G G G R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R Interpolations - Aliasing Interpolate

19. B B B B B B B B B GGG G G G G GGG G G G G GGG G G G G R R R R R R R R R R R R B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B G G G G G G G G G G G G G GG G G G G G GG G G G G G GG G G G G G GG G G G G G G R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R Interpolations - Aliasing Result Interpolate

20. Interpolations - Samples

21. Interpolation-Bilinear Bicubic • Red and Blue Kernels: • Separable 1-D filters Rw Rw = ½(Rnw+Rsw)

22. Interpolation-Bilinear Bicubic • Green kernels • 2-D filters:

23. Interpolation- Gradient Based • First, calculate Green channel: • Calculate derivates estimators • Determination of Green’s values

24. Interpolation – Evaluation Tools

25. Interpolation -Results Original Linear Kimmel

26. Outline • Introduction • Digital Cameras • Interpolations • Detecting CFA Interpolation • Results • Cracks Methods • Computers Graphics

27. Detecting CFA Interpolation • In Each pixel only one color derives from the sensor ,two others derive from interpolation from their neighbors . • The correlation are periodic. • Tampering will destroy these correlations. • Splicing together two images from different cameras will create inconsistent correlations across the composite image.

28. Detecting CFA Interpolation • Two different tools: • EM algorithm : • Produce Map of Probabilities and interpolation coefficients • Used to detect kind of interpolation • Farid’s Indicator: • Produce Map of Similarities • Used to quantify the similarity to CFA Interpolated Image

29. EM Algorithm (Expectation/Maximization): • Two possible models: • M1:the sample is linearly correlated to its neighbors • M2:the sample is not correlated to its neighbors

30. EM Algorithm (Expectation/Maximization): • f(x,y) – color channel • alpha - parameters ,where(0,0) = 0. denotes the specific correlation. • n - independent and identically samples drawn from a Gaussian distribution, with 0 mean and unknown variance

31. EM Algorithm (Expectation/Maximization): • Two-step iterative algorithm: • E-step : calculate the probability of each sample • M-step: the specific form of the correlation is estimated. • Based in Bayes rule:

32. Farid’s indicator • The similarity between the probability and a synthetic map is obtained by: • Where: • Similarity measure is phase insensitive

33. Farid’s indicator • How to use it: • CFA-Interpolated : if at least one channel is greater than threshold1 • Non CFA Interpolated: if all 3 channels are smaller than threshold2 result threshold2 threshold1 Non CFA Interpolated Unknown CFA Interpolated Ind(cfa-sf) Ind(cfa-isf)

34. Huang indicator • Motivation: Farid’s Indicator is proportional to image size. • Table of Green Channel Indicator • Huang Indicator:

35. Outline • Introduction • Digital Cameras • Interpolations • Detecting CFA Interpolation • Results • Cracks Methods • Computers Graphics

36. Results • Detecting different interpolation methods • Detecting tampering • Measuring Sensitivity and robustness

37. Detecting different interpolation methods • Hundreds of images from 2 digital cameras • Blur 3x3 • Down sampled • Cropped • Resample in CFA Interpolations

38. Detecting different interpolation methods

39. Detecting different interpolation methods

40. Detecting different interpolation methods

41. Detecting different interpolation methods

42. Detecting different interpolation methods • Coefficients are 8 to each color so we are a 24-D vector ,LDA classifier ,results: • 97% Interpolations kinds was detected • 2D projection of LDA

43. Detecting tampering • Hiding the damage of the car • Air-brushing ,smudging ,blurring and duplication

44. Detecting tampering • Result: • Left F(p) : for tampered portion • Right F(p) : for unadulterated portion

45. Measuring Sensitivity and robustness • Testing different interpolations with Farid’s indicator remember

46. Measuring Sensitivity and robustness • Testing influence of jpeg

47. Measuring Sensitivity and robustness • Testing influence of Gaussian Noise

48. Outline • Introduction • Digital Cameras • Interpolations • Detecting CFA Interpolation • Results • Crack Methods • Computer Graphics

49. Cracking • What’s a “true digital image” • General Model

50. True digital image • It was taken by a CCD/CMOS digital camera, or other device with similar function and remains intact after shooting except for embedding ownership and other routinely added information.