1. Image Hashing for DWT SPIHT Coded Images 陳慶鋒

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 Content-based image retrieval(CBIR) Content-based image retrieval(CBIR) Image hashing Image hashing

4. The significance maps from SPIHT SPIHT SPIHTInitialization Sorting pass Refinement pass Quantization-step update output: bit stream

5. 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

6. The significance maps from SPIHT examples examples

7. The significance maps from SPIHT example example110001000010 LIPLIS(A)LIS(B)

8. 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

9. 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

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 distance the autocorrelogram of 1’s of M is defined as the autocorrelogram of 1’s of M is defined as

11. The SPIHT-autocorrelogram example example 11 10 13012

12. 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.

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

14. Experimental Results Setup Setup database: 900images(100 different images and 800 attacked images) color space: YCbCr DWT: 9/7f level: 5 the thresholds: the first 3 thresholds sign maps per image: 3*3*4*3=108

15. Experimental Results Attack modes Attack modes A1 Gaussian filtering 3x3 A2 Sharpening 3x3 A3 median filter 3x3 A4 FMLR A5 random bend A6JPEG 20% A7flip A8ratation 90 degree

16. Experimental Results Example of attacked images Example of attacked images

17. Experimental Results Performance measure Performance measure The efficiency of retrieval proposed by Kankanhalli Kankanhalli 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 the performance between significance maps and SPIHT-autocorrelogram Significance mapsSPIHT-autocorrelogram T 51015205101520 Efficiency 0.9980.7730.7790.7830.994 0.8670.882 0.897

19. Experimental Results Results an example: query by 0.jpg Results an example: query by 0.jpg Significance maps rankimageL1 distance 10.jpg0 2A1_0.jpg62 3A4_0.jpg79 4A3_0.jpg84 5A2_0.jpg236 6A6_0.jpg484 7A5_0.jpg599 8A6_6.jpg836 9A4_12.jpg849 10A5_12.jpg851 11A4_7.jpg852 12A5_6.jpg853 SPIHT-autocorrelograms rankimageL1 distance 10.jpg0 2A1_0.jpg1335 3A3_0.jpg1772 4A4_0.jpg1882 5A7_0.jpg2627 6A2_0.jpg3258 7A8_0.jpg3641 8A5_0.jpg3843 9A6_0.jpg4660 10A5_1.jpg7486 11A6_1.jpg7758 12A2_1.jpg7976

20. Future work More attack modes More attack modes Reading more papers Reading more papers Comparing with papers Comparing with papers