Image Forgery JPEG Compression Based Forgery Detection

1. Image Forgery JPEG Compression Based Forgery Detection Presented by: Hilal Diab Course professor: Hagit Hel-Or 18/1/2015

2. What’s to come ? • JPEG compression algorithm • DCT • Quantization tables • Benford’s law

3. JPEG That file format for images right ? Wrong, JPEG is a compression type, similar to zip

4. JPEG (Joint Photographic Experts Group) • JPEG became an image compression standard in 1992 • JPEG is a lossy compression algorithm • JPEG can achieve 10:1 compression with little noticeable difference

5. JPEG Decreased compression from left to right.

6. JPEG Algorithm Image Color transformation DCT Quantization JPEG Encoding

7. YCbCr • RGB has a lot of redundant information

8. YCbCr

9. Downsampling Source: computerphile

10. JPEG Algorithm Image Color transformation DCT Quantization

11. 8x8

12. DCT

13. DCT

14. DCT

15. DCT

16. Quantization Tables

17. DCT

19. Luminance - Chrominance

20. JPEG Algorithm Image Color transformation DCT Quantization JPEG Encoding

21. Encoding Encode the coefficients using Huffman encoding

23. Detecting Double Compressed JPEG Images Babak Mahdian and Stanislav Saic

24. Double Quantization • Double quantization process -> artifacts in the DCT coefficients histogram.

26. Detecting Double JPEG Compression • Only check the luminance channel • Only on the lower frequencies • Denoise using an average filter • Remove decaying trends while preserving local peaks

27. Detecting Double JPEG Compression Where is the minimum of n is the length of the minimum filter

28. Classification • Use an SVM to classify if single or double JPEG compressed. • The features that were used are the normalized peak positions in H shown earlier.

29. Results

30. Cons • Classification is per quantization step • They assume that images are not in bitmap format • Not good for high quality factors • Not great results when the first quality factor is higher than the second.

31. A generalized Benford’s law for JPEG coefficients and its applications in image forensics Dongdong Fu*a, Yun Q. Shi*a, Wei Sub

32. Reminder

33. Benford’s Law

34. Based On Statistics

35. JPEG Coefficients Close! but not quite there

36. Generalized Benford’s Law

37. JPEG Identification • Based on experiments, the previous law does NOT apply on double compressed images. • We can JPEG compress a given bitmap image and check if the law applies or not.

38. Graphs

39. Results 100%

40. Estimating QF • Re-Compress an image with the same QF won’t change the first digits statistics very much

41. Double-Compression Detection

42. Cons • Using Benford’s law requires a lot more information, so this will not work on small images. • Won't detect recompression of small patches

43. JPEG Error Analysis and Its Applications to Digital Image Forensics Weiqi Luo, Member, IEEE, Jiwu Huang, Senior Member, IEEE, and Guoping Qiu, Member, IEEE

44. Porpose • Identifying JPEG images • Estimating quantization steps • Detecting quantization tables • Double JPEG compression detection

45. Observations • The AC coefficients of an image will increase in the range of (-1, +1) while decrease significantly in the union regions of (-2, -1] and [+1, +2) after JPEG compression with quantization steps that are equal to or larger than 2. • Same quantization table -> better preservation of the original image.

46. Reminder

48. Fun math (ha ha) Rounding error

49. Definitions • P , P’: the PDF of d and d’ • k: the quantization step

50. Fun math