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Image Hashing for DWT SPIHT Coded Images

Image Hashing for DWT SPIHT Coded Images. 陳慶鋒. Outline. Image hashing The significance maps from SPIHT The SPIHT-autocorrelogram Distance(similarity) measure Experimental results Future work. Image hashing. Watermarking Content-based image retrieval(CBIR) Image hashing.

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Image Hashing for DWT SPIHT Coded Images

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  1. Image Hashing for DWT SPIHT Coded Images 陳慶鋒

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

  3. Image hashing • Watermarking • Content-based image retrieval(CBIR) • Image hashing

  4. The significance maps from SPIHT • SPIHT Initialization 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. • Only the last 4 subbands are considered

  6. The significance maps from SPIHT • examples

  7. The significance maps from SPIHT • example LIP LIS(A) LIS(B)

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

  9. The SPIHT-autocorrelogram • Count the autocorrelogram of 1’s for each significance map let a significance map M be a mxm matrix , means its value

  10. The SPIHT-autocorrelogram • Count the autocorrelogram of 1’s for each significance map let a distance the autocorrelogram of 1’s of M is defined as

  11. The SPIHT-autocorrelogram • example

  12. Distance(similarity) measure • For the significance maps or the SPIHT-autocorrelograms, convert them to an one-dimension vector as our hash.

  13. Distance(similarity) measure • Distance measure using L1 distance let H and H’ be the hashes of two iamges Hi means the value of the ith entry in H the L1 distance between two hashes is defined as

  14. Experimental Results • 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. A1 Gaussian filtering 3x3 A2 Sharpening 3x3 A3 median filter 3x3 A4 FMLR A5 random bend A6 JPEG 20% A7 flip A8 ratation 90 degree Experimental Results • Attack modes

  16. Experimental Results • Example of attacked images

  17. Experimental Results • Performance measure The efficiency of retrieval proposed by 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. Significance maps SPIHT-autocorrelogram T 5 10 15 20 5 10 15 20 Efficiency 0.998 0.773 0.779 0.783 0.994 0.867 0.882 0.897 Experimental Results • Results the performance between significance maps and SPIHT-autocorrelogram

  19. SPIHT-autocorrelograms Significance maps rank rank image image L1 distance L1 distance 1 1 0.jpg 0.jpg 0 0 2 2 A1_0.jpg A1_0.jpg 1335 62 3 A4_0.jpg 79 3 A3_0.jpg 1772 4 A3_0.jpg 84 4 A4_0.jpg 1882 5 A2_0.jpg 236 5 A7_0.jpg 2627 6 A6_0.jpg 484 7 A5_0.jpg 599 6 A2_0.jpg 3258 8 A6_6.jpg 836 7 A8_0.jpg 3641 9 A4_12.jpg 849 8 A5_0.jpg 3843 10 A5_12.jpg 851 9 A6_0.jpg 4660 11 A4_7.jpg 852 10 A5_1.jpg 7486 12 A5_6.jpg 853 11 A6_1.jpg 7758 12 A2_1.jpg 7976 Experimental Results • Results an example: query by 0.jpg

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

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