Invariant Image Hashing for DWT SPIHT Coded Images

陳慶鋒陳慶鋒11/23/0411/23/04

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

the watermark is embedded in the image the watermark is embedded in the image

for copy detectionfor copy detection

measuring “originality”measuring “originality”

Content-based image retrieval(CBIR)Content-based image retrieval(CBIR)get index from the content of the imageget index from the content of the image

to find similar imagesto find similar images

measuring “similarity”measuring “similarity”

Image hashingImage hashingget hash from the content of the imageget hash from the content of the image

for copy detection and searchingfor copy detection and searching

measuring “similarity”measuring “similarity”

SPIHT

AlgorithmAlgorithm

InitializationInitialization

Sorting passSorting pass

Refinement passRefinement pass

Quantization-step updateQuantization-step update

output: bit streamoutput: bit stream

SPIHT

In sorting passIn sorting pass check the significance of node (i,j) in LIPcheck 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)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)L(i,j): O(i,j)-D(i,j)

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 Example Example

The significance maps from SPIHT Example the initial threshold is 32 Example the initial threshold is 32

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00 00

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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

LL1 1 distance:distance:

LL2 2 distance:distance:

The SPIHT-autocorrelogram

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

set a max distance set a max distance

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

The SPIHT-autocorrelogram ExampleExample

LL1 1 distance:distance:

LL2 2 distance:distance:

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11 0011 33 00

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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.

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Distance(similarity) measure

Distance measure Distance measure

LL1 1 distance vs. Weighted distancedistance vs. Weighted distance

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 defined distance between two hashes is defined

asas

the Weightedthe Weighted distance between two hashes is distance between two hashes is

defined asdefined as

Experimental Results

SetupSetupdatabase: 10 different images.database: 10 different images.

for each image,using Stirmark 3.1 and for each image,using Stirmark 3.1 and 4.0 to simulate various manipulations.4.0 to simulate various manipulations.

color space: YCbCrcolor space: YCbCrimage size: zoom all images to 512*512image size: zoom all images to 512*512DWT: 9/7fDWT: 9/7flevel: 5level: 5the thresholds: the first 3 thresholds the thresholds: the first 3 thresholds

Experimental Results The 10 imagesThe 10 images

 

Experimental Results

Performance measurePerformance measure

recall:recall:

precision:precision:

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 Results Results

Stirmark 3.1Stirmark 3.1

d=7, weighted distanced=7, weighted distance

T=1T=1 T=2T=2 T=3T=3

RecallRecall PrecisionPrecision RecallRecall PrecisionPrecision RecallRecall PrecisionPrecision

Convolution filter (2)Convolution filter (2) 11 1 11 0.5 11 0.33

Median filter (3)Median filter (3) 11 1 11 0.5 11 0.33

FMLR (1)FMLR (1) 11 1 11 0.5 11 0.33

JPEG (12)JPEG (12) 11 1 11 0.5 11 0.33

Scaling (6)Scaling (6) 11 1 11 0.5 11 0.33

Shearing (6)Shearing (6) 0.980.98 0.98 11 0.5 11 0.33

Aspect ratio (8)Aspect ratio (8) 11 1 11 0.5 11 0.33

General linear (3)General linear (3) 11 1 11 0.5 11 0.33

Rotation crop (16)Rotation crop (16) 0.860.86 086 0.890.89 0.44 0.930.93 0.31

Rotation crop scale (16)Rotation crop scale (16) 0.860.86 0.86 0.90.9 0.45 0.940.94 0.31

Cropping (9)Cropping (9) 0.660.66 0.66 0.770.77 0.38 0.840.84 0.28

Row and col removal (5)Row and col removal (5) 11 1 11 0.5 11 0.33

Geometric distortion (1)Geometric distortion (1) 11 1 11 0.5 11 0.33

Experimental Results Results Results

Stirmark 4.0Stirmark 4.0 T=1T=1 T=2T=2 T=3T=3

d=7, weighted distanced=7, weighted distance RecallRecall PrecisionPrecision RecallRecall PrecisionPrecision RecallRecall PrecisionPrecision

Affine (8)Affine (8) 0.890.89 0.89 0.930.93 0.46 0.940.94 0.31

Convolution filter (2)Convolution filter (2) 0.650.65 0.65 0.70.7 0.35 0.80.8 0.27

Cropping (9)Cropping (9) 0.20.2 0.2 0.340.34 0.17 0.420.42 0.14

JPEG (12)JPEG (12) 11 1 11 0.5 11 0.33

Median filter (4)Median filter (4) 0.950.95 0.95 0.980.98 0.49 11 0.33

Noise (6)Noise (6) 0.370.37 0.37 0.420.42 0.21 0.570.57 0.19

PSNR (11)PSNR (11) 11 1 11 0.5 11 0.33

Rescaling (6)Rescaling (6) 11 1 11 0.5 11 0.33

Remove line (10)Remove line (10) 11 1 11 0.5 11 0.33

Rotation (16)Rotation (16) 0.790.79 0.79 0.840.84 0.42 0.860.86 0.27

Rotation crop (10)Rotation crop (10) 0.970.97 0.97 0.970.97 0.48 0.970.97 0.32

Rotation crop scale (10)Rotation crop scale (10) 0.950.95 0.95 0.960.96 0.48 0.970.97 0.32

Self similarity (3)Self similarity (3) 11 1 11 0.5 11 0.33

Experimental Results

Failed in Stirmark 3.1Failed in Stirmark 3.1

Rotation Crop 30°

Rotation Crop Scale 30°

Cropping 20%

Experimental Results Failed in Stirmark 4.0Failed in Stirmark 4.0

Convolution filter

Rotation 5° Cropping 75% Noise 40%

Future work

Larger databaseLarger database Reading more papersReading more papers Comparing with papersComparing with papers