1. Detection of Image Alterations Using Semi-fragile Watermarks
Eugene T. Lin†, Christine I. Podilchuk‡ and Edward J. Delp†
†Purdue University
School of Electrical and Computer Engineering
Video and Image Processing Laboratory (VIPER)
West Lafayette, Indiana
‡Bell Laboratories, Lucent Technologies
Murray Hill, New Jersey

2. Overview Introduction Image authentication Fragile watermarks
Robust watermarks
Semi-fragile watermarks
Description of proposed technique
Results
Conclusion

3. Image Authentication Identify the source of an image
Determine if the image has been altered
If so, locate regions where alterations have occurred
Authentication watermark
watermark is imperceptible under normal observation
allows user to determine if image has been altered after mark embedding

4. Fragile Watermarks
Watermark is rendered undetectable after slightest modifications to marked content
Typically able to localize alterations with high degree of precision
Sensitivity achieved through use of hash functions
Problem: if lossy compression is applied to marked image, mark is destroyed even though compressed image remains perceptually similar

5. Robust Watermarks Resists removal attempts
Examines large regions of image, limited localization of alterations
Robustness typically achieved through spread-spectrum techniques
Problem: robust watermark may remain even after alterations that change the visual message conveyed by the image

6. Semi-Fragile Watermarks
Able to detect and localize significant “information altering” transformations (feature replacement)
Able to tolerate some degree of “information preserving” transformations (lossy compression)
Suitable in authentication applications where legitimate use includes lossy compression or other image adjustment by users

7. Semi-Fragile Watermarks
Challenges for fragile watermark  semi-fragile watermark:
LSB plane embedding not tolerant to compression
Cryptographic hash functions too sensitive
Challenges for robust watermark  semi-fragile watermark:
Reduce region size used in mark detection but retain enough SNR to achieve reliable detection
Boundary effects

8. Description of Proposed Technique
Watermark construction
DCT construction, spatial embedding
Watermark detection
Based on differences of adjacent pixel values
Most natural images contain large regions of relatively smooth features

9. Watermark Construction
DCT Watermark Generation

10. Watermark Construction
After watermark is constructed in DCT domain, it is transformed to spatial domain and embedded
DCT watermark Generation
IDCT
Original Image +
Marked Image W X
Y=X+W

11. Watermark Detection
Independent detection performed on each block, for localizing altered blocks
Define two operators:

12. Example of Differential Operators

13. Watermark Detection Tb = Block of image being tested
Wb = Corresponding block of watermark image
Detector uses both row and column differences:

14. Block Test Statistic
Tb* and Wb* are correlated to compute block test statistic b:
b  T: Block is likely authentic
b < T: Block is likely altered.

15. Results - Gradient Original “Gradient” Altered “Gradient”
Total Blocks: 682, Altered:300 (44%)
Detector Block size:16x16, embedding =5.0

16. Results - Gradient

17. Results - Gradient

18. Results - Sign Original “Sign” Altered “Sign”
Total Blocks: 1536, Altered:77 (5%)
Detector Block size:16x16, embedding =5.0

19. Results - Sign

20. Results - Sign

21. Results - Money Original “Money” Altered “Money”
Total Blocks: 570, Altered:143 (25%)
Detector Block size:16x16, embedding =5.0

22. Results - Money

23. Results - Money

24. Results - Girls  Original “Girls” Altered “Girls” 
Total Blocks: 5704, Altered:951 (17%)
Detector Block size:16x16, embedding =5.0

25. Results - Girls

26. Results - Girls

27. Detection Performance
Embed: =5.0
Detection:
T=0.1
blocksize=16x16
JPEG-60
bitrate=0.90 bpp
93% correct detection
4% false positive
17% misses

28. Conclusions
A semi-fragile watermarking technique was proposed which classifies about 70%of blocks correctly for moderate JPEG compression, 90% for light JPEG compression
Detector has problems with edges and textures
Future work:
Integrate a visual model to embed mark at higher strengths in textured areas