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Image Forgery JPEG Compression Based Forgery Detection

Image Forgery JPEG Compression Based Forgery Detection. Presented by: Hilal Diab Course professor: Hagit Hel-Or. 18/1/2015. What’s to come ?. JPEG compression algorithm DCT Quantization tables Benford’s law. JPEG. That file format for images right ?.

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Image Forgery JPEG Compression Based Forgery Detection

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  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

  18. Luminance - Chrominance

  19. JPEG Algorithm Image Color transformation DCT Quantization JPEG Encoding

  20. Encoding Encode the coefficients using Huffman encoding

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

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

  23. 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

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

  25. 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.

  26. Results

  27. 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.

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

  29. Reminder

  30. Benford’s Law

  31. Based On Statistics

  32. JPEG Coefficients Close! but not quite there

  33. Generalized Benford’s Law

  34. 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.

  35. Graphs

  36. Results 100%

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

  38. Double-Compression Detection

  39. 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

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

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

  42. 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.

  43. Reminder

  44. Fun math (ha ha) Rounding error

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

  46. Fun math

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