1. Invariant Image Hashing for DWT SPIHT Coded Images 陳慶鋒11/23/04

2. Outline Image hashing Image hashing The significance maps from SPIHT The significance maps from SPIHT The SPIHT-autocorrelogram The SPIHT-autocorrelogram Distance(similarity) measure Distance(similarity) measure Experimental results Experimental results Future work Future work

3. Image hashing Watermarking Watermarking the watermark is embedded in the image for copy detection measuring “originality” Content-based image retrieval(CBIR) Content-based image retrieval(CBIR) get index from the content of the image to find similar images measuring “similarity” Image hashing Image hashing get hash from the content of the image for copy detection and searching measuring “similarity”

4. SPIHT Algorithm AlgorithmInitialization Sorting pass Refinement pass Quantization-step update output: bit stream

5. SPIHT In sorting pass In sorting pass check the significance of node (i,j) in LIP check the significance of node (i,j) in LIP check the significance of O(i,j) in LIS (A type) check the significance of O(i,j) in LIS (A type) check the significance of L(i,j) in LIS (B type) check the significance of L(i,j) in LIS (B type) O(i,j): set of coordinates of all offspring of node (i,j) D(i,j): set of coordinates of all descendants of node (i,j) D(i,j): set of coordinates of all descendants of node (i,j) L(i,j): O(i,j)-D(i,j)

6. The significance maps from SPIHT In sorting pass, we can get the significance of each entry in LIP and LIS(A type and B type). So we form the significance maps according to the above property. In sorting pass, we can get the significance of each entry in LIP and LIS(A type and B type). So we form the significance maps according to the above property. Only the last 4 subbands are considered Only the last 4 subbands are considered

7. The significance maps from SPIHT Example Example

8. The significance maps from SPIHT Example the initial threshold is 32 Example the initial threshold is 32110001000010 LIPLIS(A)LIS(B)

9. The SPIHT-autocorrelogram Histogram-based method in CBIR Histogram-based method in CBIR ex: CCV,color correlogram,etc property: contain both color and spatial information resistant to geometric distortion resistant to geometric distortion

10. The SPIHT-autocorrelogram Count the autocorrelogram of 1’s for each significance map Count the autocorrelogram of 1’s for each significance map let a significance map M be a mxm matrix, means its value, means its value L 1 distance: L 1 distance: L 2 distance: L 2 distance:

11. The SPIHT-autocorrelogram Count the autocorrelogram of 1’s for each significance map Count the autocorrelogram of 1’s for each significance map set a max distance the autocorrelogram of 1’s of M is defined as the autocorrelogram of 1’s of M is defined as

12. The SPIHT-autocorrelogram Example Example L 1 distance: L 2 distance: 11 10 12112 1110 13012

13. Distance(similarity) measure For the significance maps or the SPIHT- autocorrelograms, convert them to an one- dimension vector as our hash. For the significance maps or the SPIHT- autocorrelograms, convert them to an one- dimension vector as our hash. 11 10 21 11100110 2101

14. Distance(similarity) measure Distance measure Distance measure L 1 distance vs. Weighted distance let H and H’ be the hashes of two iamges let H and H’ be the hashes of two iamges H i means the value of the ith entry in H H i means the value of the ith entry in H the L 1 distance between two hashes is defined as the Weighted distance between two hashes is defined as

15. Experimental Results Setup Setup database: 10 different images. for each image,using Stirmark 3.1 and 4.0 to simulate various manipulations. color space: YCbCr image size: zoom all images to 512*512 DWT: 9/7f level: 5 the thresholds: the first 3 thresholds

16. Experimental Results The 10 images The 10 images

17. Experimental Results Performance measure Performance measure recall: recall: precision: precision: N: the number of ground truth T: the first T similar image we consider in retrieval n: the number of matched images in retrieval

18. Experimental Results Results Results Stirmark 3.1 d=7, weighted distance T=1T=2T=3 RecallPrecisionRecallPrecisionRecallPrecision Convolution filter (2) 1110.510.33 Median filter (3) 1110.510.33 FMLR (1) 1110.510.33 JPEG (12) 1110.510.33 Scaling (6) 1110.510.33 Shearing (6) 0.980.9810.510.33 Aspect ratio (8) 1110.510.33 General linear (3) 1110.510.33 Rotation crop (16) 0.860860.890.440.930.31 Rotation crop scale (16) 0.860.860.90.450.940.31 Cropping (9) 0.660.660.770.380.840.28 Row and col removal (5) 1110.510.33 Geometric distortion (1) 1110.510.33

19. Experimental Results Results Results Stirmark 4.0 T=1T=2T=3 d=7, weighted distance RecallPrecisionRecallPrecisionRecallPrecision Affine (8) 0.890.890.930.460.940.31 Convolution filter (2) 0.650.650.70.350.80.27 Cropping (9) 0.20.20.340.170.420.14 JPEG (12) 1110.510.33 Median filter (4) 0.950.950.980.4910.33 Noise (6) 0.370.370.420.210.570.19 PSNR (11) 1110.510.33 Rescaling (6) 1110.510.33 Remove line (10) 1110.510.33 Rotation (16) 0.790.790.840.420.860.27 Rotation crop (10) 0.970.970.970.480.970.32 Rotation crop scale (10) 0.950.950.960.480.970.32 Self similarity (3) 1110.510.33

20. Experimental Results Failed in Stirmark 3.1 Failed in Stirmark 3.1 Rotation Crop 30° Rotation Crop Scale 30° Cropping 20%

21. Experimental Results Failed in Stirmark 4.0 Failed in Stirmark 4.0 Convolution filter Rotation 5°Cropping 75%Noise 40%

22. Future work Larger database Larger database Reading more papers Reading more papers Comparing with papers Comparing with papers