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Image Forgery Identification Using JPEG Intrinsic Fingerprints

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EE398: Image and Video Compression ... JPEG compress tamper save bitmap. Q table available. Develop forgery detection algorithms ... – PowerPoint PPT presentation

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Title: Image Forgery Identification Using JPEG Intrinsic Fingerprints


1
Image Forgery Identification Using JPEG Intrinsic
Fingerprints
  • Group
  • Ayichew Hailu
  • Srinivasa Rangan
  • Ashutosh Garg

Advisors Dr. Bernd Girod Dr. Min Wu David
Varodayan
2
Project Goals
  • Develop algorithms to detect and classify
    possible tamperings
  • Motivation Image validation
  • Security, law enforcement intelligence
  • Possible tampering
  • Cropping, rotation, blurring, sharpening,
    insertion etc.

3
Assumptions
  • One forgery per image
  • Grayscale images
  • Algorithms easily extendible to color images
  • Restrict forgeries
  • Helps achieve forgery classification
  • Limited to copy-paste, rotation, cropping and
    brightness

4
Problem Approach
  • Phase 1
  • JPEG compress ? tamper ? save bitmap
  • Q table available
  • Develop forgery detection algorithms
  • Phase 2
  • Estimate Q table based on JPEG standard Annex K
  • Phase 3
  • JPEG compress ? tamper ? JPEG compress
  • Estimate primary Q table

5
Forgery measure
  • For each block
  • Compute DCT Transform
  • Obtain metric based on remainder w.r.t. entries
    of Q matrix
  • where
  • and gives the absolute difference between a and
    the closest multiple of b

6
Forgery measure cont.
  • Sum across blocks to obtain measure for image
  • Compare against threshold
  • Obtained through training

7
Estimating Threshold
Effect of Threshold on the Performance of Method
8
Copy-Paste Forgery
9
Original
  • Copy-Paste Forgery

Forgery
10
Forgery
  • Copy-Paste Forgery

Result
11
Cropping
  • Search 8x8 block to fit DCT grid
  • if successful, identify as cropping
  • Repeat on last block
  • Exception
  • cropping preserves DCT grid

12
Rotation
  • Rotate by 90 check distortion
  • If lt threshold, declare rotation forgery
  • Test fails for 180 rotation

Brightness
  • Checks for high level of brightness changes
  • Saturation and threshold based

13
Estimating Q Matrix for Single Compressed JPEG
Image
  • For each DCT coefficient
  • Obtain histogram
  • Quantization step Distance between successive
    peaks of histogram
  • Compute power spectral density Ye, 2007
  • Estimate first 3X3 coefficients of Q matrix
  • Obtain other values by finding closest match to
    standard matrices using lookup table

14
Histogram Plots
15
Performance
16
Test Result
Cropping forgery
Copy-Paste forgery
Rotation forgery
False Positive Rate and False Negative Rate
versus Quality Factor
17
Double JPEG Compression
  • Primary quantization matrix estimated
  • Fridrich, 2003
  • Reliable estimate if second QF is greater than
    first
  • Estimate used for previous algorithms
  • Successfully tested on limited images

18
Future Work
  • Extend methods to classifying other kinds of
    forgeries.
  • Add more features in Q matrix approach to
    improve accuracy

19
Conclusions
  • Algorithm detects forgeries with good accuracy
    for both single and double JPEG compressed images
  • Accuracy improves with higher quality factor
  • Cropping and rotation are easiest to detect while
    copy-paste is hardest to detect

20
  • Thank You

21
References
  • 1 F. Fridrich, D. Soukal, and J. Lukas,
    Detection of Copy-Move Forgery in Digital
    Images, Digital Forensic Research Workshop,
    Cleveland, USA, Aug. 2003.
  • 2 W. Luo, Z. Qu, J. Huang, G. Qiu, "A Novel
    Method for Detecting Cropped and Recompressed
    Image Block," Acoustics, Speech and Signal
    Processing, 2007. ICASSP 2007. IEEE International
    Conference on , vol.2, no., pp.II-217-II-220,
    15-20 April 2007.
  • 3 J. Lukas and J. Fridrich, Estimation of
    Primary Quantization Matrix in Double Compressed
    JPEG Images, Proc. of the Digital Forensics
    Research Workshop, Cleveland, OH, Aug. 2003.
  • 4 G. Schaefer, and M. Stich, UCID An
    Uncompressed Color Image Database, Technical
    Report, School of Computing and Mathematics,
    Nottingham Trent University, U.K., 2003.
  • 5 A Swaminathan, M. Wu, K. Liu J. R., "Digital
    Image Forensics via Intrinsic Fingerprints,"
    Information Forensics and Security, IEEE
    Transactions on , vol.3, no.1, pp.101-117, March
    2008.
  • 6 S. Ye, Q. Sun, E. and Chang, "Detecting
    Digital Image Forgeries by Measuring
    Inconsistencies of Blocking Artifact," Multimedia
    and Expo, 2007 IEEE International Conference on ,
    vol., no., pp.12-15, 2-5 July 2007.

22
Appendix
23
Rotation
  • A rot90(X) ( X rot90(A) )T
  • A rot180(X) AT
  • ( rot90(X) rot90(A) )T AT
  • rot90(A)T ( A rot90(X) )T
  • rot90(A)T X rot90(A)
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