Image Hashing for DWT SPIHT Coded Images

陳慶鋒陳慶鋒

Outline

Image hashingImage hashing The significance maps from SPIHTThe significance maps from SPIHT The SPIHT-autocorrelogramThe SPIHT-autocorrelogram Distance(similarity) measureDistance(similarity) measure Experimental resultsExperimental results Future workFuture work

Image hashing

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

The significance maps from SPIHT

SPIHTSPIHT

InitializationInitialization

Sorting passSorting pass

Refinement passRefinement pass

Quantization-step updateQuantization-step update

output: bit streamoutput: bit stream

The significance maps from SPIHT In sorting pass, we can get the significance In sorting pass, we can get the significance

of each entry in LIP and LIS(A type and B of each entry in LIP and LIS(A type and B type). So we form the significance maps type). So we form the significance maps according to the above property. according to the above property.

Only the last 4 subbands are consideredOnly the last 4 subbands are considered

The significance maps from SPIHT examplesexamples

The significance maps from SPIHT exampleexample

11 11

00 00

00 11

00 00

00 00

11 00

LIP LIS(A) LIS(B)

The SPIHT-autocorrelogram

Histogram-based method in CBIRHistogram-based method in CBIRex: CCV,color correlogram,etcex: CCV,color correlogram,etc

property: contain both color and spatial property: contain both color and spatial information information

resistant to geometric distortionresistant to geometric distortion

The SPIHT-autocorrelogram

Count the autocorrelogram of 1’s for each Count the autocorrelogram of 1’s for each significance mapsignificance map

let a significance map let a significance map M M be a be a mmxxm m matrixmatrix

, means its value, means its value

The SPIHT-autocorrelogram

Count the autocorrelogram of 1’s for each Count the autocorrelogram of 1’s for each significance mapsignificance map

let a distance let a distance

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

The SPIHT-autocorrelogram exampleexample

11 11

11 0011 33 00

11 22

Distance(similarity) measure

For the significance maps or the SPIHT-For the significance maps or the SPIHT-autocorrelograms, convert them to an one-autocorrelograms, convert them to an one-dimension vector as our hash.dimension vector as our hash.

Distance(similarity) measure

Distance measureDistance measureusing Lusing L1 1 distancedistance

let H and H’ be the hashes of two iamgeslet H and H’ be the hashes of two iamges

HHi i means the value of the means the value of the iith entry in Hth entry in H

the Lthe L1 1 distance between two hashes is distance between two hashes is

defined defined as as

Experimental Results

SetupSetupdatabase: 900images(100 different images and 800 database: 900images(100 different images and 800

attacked images)attacked images)color space: YCbCrcolor space: YCbCrDWT: 9/7fDWT: 9/7flevel: 5level: 5the thresholds: the first 3 thresholds the thresholds: the first 3 thresholds sign maps per image: 3*3*4*3=108sign maps per image: 3*3*4*3=108

Experimental Results

Attack modesAttack modes

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 Example of attacked imagesExample of attacked images

Experimental Results

Performance measurePerformance measureThe efficiency of retrieval proposed by The efficiency of retrieval proposed by

KankanhalliKankanhalli

N: the number of ground truthN: the number of ground truthT: the first T similar image we consider in retrievalT: the first T similar image we consider in retrievaln: the number of matched images in retrievaln: the number of matched images in retrieval

Experimental Results

ResultsResults

the performance between significance maps and the performance between significance maps and

SPIHT-autocorrelogramSPIHT-autocorrelogram

　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 Results an example: query by 0.jpgan example: query by 0.jpg

Significance maps

rank image L1 distance

1 0.jpg 02 A1_0.jpg 623 A4_0.jpg 794 A3_0.jpg 845 A2_0.jpg 2366 A6_0.jpg 4847 A5_0.jpg 5998 A6_6.jpg 8369 A4_12.jpg 849

10 A5_12.jpg 85111 A4_7.jpg 85212 A5_6.jpg 853

SPIHT-autocorrelograms

rank image L1 distance

1 0.jpg 02 A1_0.jpg 13353 A3_0.jpg 17724 A4_0.jpg 18825 A7_0.jpg 26276 A2_0.jpg 32587 A8_0.jpg 36418 A5_0.jpg 38439 A6_0.jpg 466010 A5_1.jpg 748611 A6_1.jpg 775812 A2_1.jpg 7976

Future work

More attack modesMore attack modes Reading more papersReading more papers Comparing with papersComparing with papers