1 perceptually based methods for robust image hashing vishal monga committee members: prof. ross...
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1
Perceptually Based Methods for Perceptually Based Methods for Robust Image HashingRobust Image Hashing
Vishal MongaVishal MongaCommittee Members:
Prof. Ross Baldick
Prof. Brian L. Evans (Advisor)
Prof. Wilson S. Geisler
Prof. Joydeep Ghosh
Prof. John E. Gilbert
Prof. Sriram Vishwanath
Ph.D. Qualifying ExamCommunications, Networks, and Systems AreaDept. of Electrical and Computer Engineering
The University of Texas at AustinApril 14th , 2004
2
• Introduction
• Related work– Digital signature techniques for image authentication– Robust feature extraction from images– Open research issues
• Expected contributions– Framework for robust image hashing using feature points– Clustering algorithms for feature vector compression– Image authentication under geometric attacks via structure
matching
• Conclusion
OutlineOutline
3
• Hash function: Projects value from set with large (possibly infinite) number of members to set with fixed number of (fewer) members in irreversible manner– Provides short, simple
representation of large digital message
– Hash Scheme – Sum of ASCII codes of characters in a name computed modulo N (= 7) a prime number
Hash ExampleHash ExampleIntroduction
NameHash Value
Ghosh 1Monga 2Baldick 3
Vishwanath 3Evans 5Geisler 5Gilbert 6
Database name search example
4
Image Hashing: MotivationImage Hashing: Motivation
Introduction
• Hash functions– Fixed length binary string extracted from a message
– Used in compilers, database searching, cryptography
– Cryptographic hash: security applications e.g. message authentication, ensuring data integrity
• Traditional cryptographic hash– Not suited for multimedia very sensitive to input, i.e.
change in one input bit changes output dramatically
• Need for robust perceptual image hashing
– Perceptual: based on human visual system response
– Robust: hash values for “perceptually identical” images must be the same (with a high probability)
5
Image Hashing: MotivationImage Hashing: Motivation
• Applications– Image database search and indexing
– Content dependent key generation for watermarking
– Robust image authentication: hash must tolerate incidental modifications yet be sensitive to content changes
Introduction
Same hash value h1
Different hash valuesh2
Original ImageJPEG Compressed Tampered
6
Perceptual Hash: Desirable PropertiesPerceptual Hash: Desirable Properties
• Perceptual robustness
• Fragility to distinct inputs
• Randomization
– Necessary in security applications
to minimize vulnerability against
malicious attacks
Pr( ( ) ( )) 1simH I H I
Pr( ( ) ( )) 1diffH I H I
Introduction
Symbol Meaning
H(I)
Hash value extracted from
image I
Isim
Image identical in appearance to I
Idiff
Image clearly distinct in
appearance w.r.t I
m
Length of hash (in bits)
{0,1} ,2
1))(Pr( m
mvvIH
7
• Introduction
• Related work– Digital signature techniques for image authentication– Robust feature extraction from images– Open research issues
• Expected contributions– Framework for robust image hashing using feature points– Clustering algorithms for feature vector compression– Image authentication under geometric attacks via structure
matching
• Conclusion
OutlineOutline
8
Content Based Digital SignaturesContent Based Digital Signatures
Related Work
• Goal– Authenticate image based on extracted signature
• Image statistics based on– Intensity histograms of image blocks [Schneider et al., 1996] – mean, variance and kurtosis of intensity values
extracted from image blocks and compare then to statistics of reference image [Kailasanathan et al., 2001]
• Drawbacks– Easy to modify the image without altering its intensity
histogram scheme is less secure
– Intensity statistics can be altered easily without significantly changing the image appearance
9
Content Based Digital Signatures…Content Based Digital Signatures…
Related Work
• Feature point based methods– Wavelet based corner detection [Bhatacherjee et al., 1998]
– Canny edge detection [Dittman et al., 1999]
– Apply public key encryption on the features to arrive at the digital signature
• Relation based methods [Lin & Chang 2001]
– Invariant relationship between discrete cosine transform (DCT) coefficients of two different blocks
• Common characteristic of above methods– work well for some attacks viz. JPEG compression
– still sensitive to several incidental modifications that do not alter the image appearance
10
Robust Image Hashing: Method # 1Robust Image Hashing: Method # 1
Related Work
• Image statistics vector from wavelet decomposition of image [Venkatesan et al., 2000]
– Averages of wavelet coefficients in coarse sub-bands and variances in other sub-bands
Extract Statistics Vector and Quantize
[00100101 | 01110100|1……………00111001 | 001010]
Error Correction Decoding
[00100101…… 011] Hash Value
Vertical freqs. Horizontal freqs. Diagonal freqs. Coarse Details
11
Robust Image Hashing: Method # 2Robust Image Hashing: Method # 2
Related Work
• Preserve magnitude of low frequency DCT coefficients [Fridrich et al., 2001]
– Survives JPEG compression, linear filtering attacks
– Very sensitive to geometric distortions (local & global)
• Randomize using a secret key K– Generate N random smooth patterns P(i), i = 1,…, N
– Take vectorized dot product of low frequency DCT coefficients (in block B) with random patterns and use threshold Th to obtain N bits bi
0 , |.| )( ii bThPBif
1 , |.| )( ii bThPBif
Back
12
Robust Image Hashing: Method # 3Robust Image Hashing: Method # 3
Related Work
• Invariance of coarse wavelet coefficients [Mihcak et al., 2001]
• Key observation– Main geometric features of image stay
invariant under small perturbations to image
• Hash algorithm– Threshold wavelet coefficients of DC sub-band
(coarse robust features) to obtain a binary matrix– Perform filtering and re-thresholding to iteratively arrive
at binary map which is then used as the hash– Iterative procedure is designed so as to preserve
significant image geometry
DC sub-band
3- level Haar wavelet decomposition
Back
13
Robust Digital Signature: Method # 4Robust Digital Signature: Method # 4
Related Work
• Interscale relationship of wavelet coefficients[Lu & Liao, 2003]
– Magnitude difference between a parent node and its four child nodes is difficult to destroy (alter) under content-preserving manipulations
– s – wavelet scale, o – orientation, 0 ≤ i, j ≤ 1
|)2,2(||),(| ,1, jyixwyxw osos
2-D wavelet decomposition tree
w0,0(x,y)
w1,3(2x+1,2y+1)w1,2(2x,2y+ 1)w1,1(2x+1,2y)w1,0(2x,2y)
14
• A robust feature point scheme for hashing – Inherent sensitivity to content-changing manipulations e.g.
could be useful in authentication– Representation of image content robust to both global and
local geometric distortions– Preferably use properties of the human visual system
• Trade-offs in image hashing– Robustness vs. Fragility, Randomness– Question: Minimum length of the final hash value (binary
string) needed to meet the above goals ?
• Randomized algorithms for secure image hashing
Open IssuesOpen IssuesRelated Work
Contribution 1
Contribution 3
Contribution 1
Contribution 2
Contribution 2
15
• Introduction
• Related Work– Digital signature techniques for Image Authentication– Robust feature extraction from Images– Open research issues
• Expected contributions– Framework for robust image hashing using feature points– Clustering algorithms for feature vector compression– Image authentication under geometric attacks via structure
matching
• Conclusion
OutlineOutline
16
• Proposed two-stage hash algorithm
Hashing FrameworkHashing Framework
Expected Contribution #1
• Feature vectors extracted from “perceptually identical” images must be close in a distance metric
Final Hash
Compression
ctorsfeature ve
similarofClustering
ctorfeature ve
obust visually rExtract
Input Image I
17
Hypercomplex or End-stopped cellsHypercomplex or End-stopped cells
• Develop filters/kernels that capture this behavior• To maintain robustness to changes in image resolution, – Wavelet based approach is needed
• Cells in the visual cortex that help in object recognition
• Respond strongly to line end-points, corners and points of high curvature [Hubel et al. 1965, Dobbins 1989]
“End-stopping and Image Geometry”, Dobbins, 1989
18
End-Stopped Wavelet BasisEnd-Stopped Wavelet Basis
• Morlet wavelets [Antoine et al., 1996]
– To detect linear (or curvilinear) structures having a specific orientation
• End-stopped wavelet [Vandergheynst et al., 2000]
– Apply First Derivative of Gaussian (FDoG) operator to detect end-points of structures identified by Morlet wavelet
))(( )(22 ||
2
1||
2
1. xkxk o
ox
eee jM
x – (x,y) 2-D spatial co-ordinates
ko – (k0, k1) wave-vector of the mother wavelet
Orientation control -0
11tank
k
19
End-Stopped Wavelets…ExampleEnd-Stopped Wavelets…Example
• Morlet Wavelet along the u-axis– Detects vertically oriented linear structures
• FDoG operator along frequency axis v– Applied on the Morlet wavelet to detect end-points and corners
Synthetic L-shaped image Response of Morlet wavelet, orientation = 0 degrees
Response of the end-stopped wavelet
20
Computing Wavelet TransformComputing Wavelet Transform
• Generalize end-stopped wavelet
• Employ the wavelet family
– Scale parameter = 2, i – scale of the wavelet – Discretize orientation range [0,π ] into M intervals i.e. – θk = (k π/M ), k = 0, 1, … M - 1
• Finally, the wavelet transform is given by
))x;(()( x)( ME oFDoG
,, )),,((( Ziyx ki
E
)),,((* ),(),,( 111111 dydxyyxxyxIyxW iEi
Expected Contribution #1
21
Proposed Feature Detection MethodProposed Feature Detection Method[Monga & Evans, 2004][Monga & Evans, 2004]
1. Compute wavelet transform at suitably chosen scale i for several different orientations
2. Significant feature selection: Locations (x,y) in the image that are identified as candidate feature points satisfy
3. Avoid trivial (and fragile) features: Qualify a location as a final feature point if
),,( max ),,( ''
),(
*
),(''
yxWyxW iNyx
iyx
TyxWi ),,( max *
Expected Contribution #1
• Randomization: Partition the image into N random regions using a secret key K, extract features from each random region
• Probabilistic Quantization: Quantize feature vector based on distribution (histogram) of image feature points to enhance robustness
22
Iterative Feature Extraction AlgorithmIterative Feature Extraction Algorithm [Monga & Evans, 2004][Monga & Evans, 2004]
1. Extract feature vector f of length P from image I, quantize f probabilistically to obtain a binary string bf
1 (increase count*)
2. Remove “weak” image geometry: Compute 2-D order statistics (OS) filtering of I to produce Ios = OS(I;p,q,r)
3. Preserve “strong” image geometry: Perform low-pass linear shift invariant (LSI) filtering on Ios to obtain Ilp
4. Repeat step 1 with Ilp to obtain bf2
5. IF (count = MaxIter) go to step 6.
ELSE IF D(bf1, bf
2) < ρ go to step 6.
ELSE set I = Ilp and go to step 1.
6. Set fv(I) = bf2
Expected Contribution #1
MaxIter, ρ and P are algorithm parameters. * count = 0 to begin with
fv(I) denotes quantized feature vector
D(.,.) – normalized Hamming distance between its arguments
23
Preliminary Results: Feature ExtractionPreliminary Results: Feature Extraction
Original ImageJPEG, QF = 10
Expected Contribution #1
AWGN, σ = 20
Image Features at Algorithm Convergence
24
Preliminary Results: Feature ExtractionPreliminary Results: Feature Extraction
• Quantized Feature Vector ComparsionD(fv(I), fv(Isim)) < 0.2D(fv(I), fv(Idiff)) > 0.3
*Attack Lena Bridge PeppersJPEG, QF = 10 0.04 0.04 0.06AWGN, σ = 20 0.04 0.03 0.02
Contrast Enhancement
0 0.06 0.04
Gaussian Smoothing
0.01 0.03 0.05
Median Filtering 0.02 0.03 0.07Scaling by 50% 0.08 0.14 0.11Rotation by 20 0.12 0.15 0.14Rotation by 50 0.18 0.20 0.19
Cropping by 10% 0.12 0.13 0.15Cropping by 20% 0.21 0.22 0.24
Table 1. Comparison of quantized feature vectors
Normalized Hamming distance between quantized feature vectors of original and attacked images
*Attacked images generated by Stirmark benchmark software
Expected Contribution #1
25
Preliminary Results: Feature ExtractionPreliminary Results: Feature Extraction
Attack
Thresholding of coarse wavelet
coefficients (Mihcak et al.)
Preserve low freq, DCT coefficients (Fridrich et al.)
Proposed feature point
detector
JPEG, QF = 10 YES YES YESAWGN, σ = 20 YES NO YES
Gaussian Smoothing
YES YES YES
Median Filtering YES NO YESScaling 50% YES YES YES
Rotation 2 degrees YES NO YESCropping 10% YES NO YESCropping 20% YES NO NO* Small object
additionNO YES NO
* Tamper with facial features
YES YES NO
Expected Contribution #1
YES survives
attack, i.e. hash was invariant
*content changing
manipulations, should be detected
26
HighlightsHighlights
Expected Contribution # 1
• Framework for image hashing using feature points– Two stage hash algorithm– Any visually robust feature point detector is a good candidate to
be used with the iterative algorithm
• Trade-offs facilitated– Robustness vs. Fragility: select feature points such that
T1, T2 large enough ensures that features are retained in several attacked versions of the image, else removed easily
– Robustness vs. Randomization: number of random regionsUntil N < Nmax, robustness largely preserved else random regions shrink to the extent that they do not contain significant chunks of image geometry
21 ),,( max TyxWT i
27
Feature Vector CompressionFeature Vector Compression
Expected Contribution # 2
• Goals in compressing to a final hash value– Cancel small perturbations between feature vectors of
“perceptually identical” images– Maintain fragility to distinct inputs– Retain and/or enhance randomness properties for secure
hashing
• Problem statement: Retain perceptual significance– Let (li, lj) denote vectors in the metric space of feature
vectors V and 0 < ε < δ, then it is desired
)()( ),( jiji lClCthenllDif
)()( ),( jiji lClCthenllDif
28
Possible SolutionsPossible Solutions
• Error correction decoding [Venkatesan et al., 2000]
– Applicable to binary feature vectors– Break the vector down to segments close to the length of
codewords in a suitably chosen error-correcting code
• More generally vector quantization/clustering– Minimize an “average distance” to achieve compression close
to the rate distortion limit
– P(l) – probability of occurrence of vector l, D(.,.) distance metric defined on the feature vectors
– ck – codewords/cluster centers, Sk – kth cluster
),( )(min1
0
K
kk
Sl
clDlPk
Expected Contribution # 2
29
Is Average Distance the Appropriate Cost for the Is Average Distance the Appropriate Cost for the Hashing Application?Hashing Application?
• Problems with average distance VQ– No guarantee that “perceptually distinct” feature vectors
indeed map to different clusters – no straightforward way to trade-off between the two goals
– Must decide number of codebook vectors in advance – Must penalized some errors harshly e.g. if vectors really
close are not clustered together, or vectors very far apart are compressed to the same final hash value
• Define alternate cost function for hashing– Develop clustering algorithm that tries to minimize that
cost
Expected Contribution # 2
30
Cost Function for Feature Vector Compression Cost Function for Feature Vector Compression
• Define joint cost matrices C1 and C2 (n x n)
– n – total number of vectors be clustered, C(li), C(lj) denote the clusters that these vectors are mapped to
• Exponential cost – Ensures that severe penalty is associated if feature vectors
far apart and hence “perceptually distinct” are clustered together
otherwise 0
)()(,),( if ),(
),(
1jiji
llD lClCllDjic
ji
otherwise 0
)()(,),( if ),(
),(
2jiji
llD lClCllDjic
ji
Expected Contribution # 2
α > 0, Г > 1
are algorithm parameters
31
Cost Function for Feature Vector Compression Cost Function for Feature Vector Compression
• Further define S1 as *S2 is defined
similarly
• Normalize to get ,
• Then, minimize the “expected” cost
– p(i) = p(li), p(j) = p(lj)
i j
jis
jicjic
),(
),(),(
1
11
i j
jicjicjpipE ),(),()()(CC 2121
1C
2C
i j
jis
jicjic
),(
),(),(
2
22
Expected Contribution # 2
otherwise 0
),( if ),(
),(
1
ji
llD llDjis
ji
32
Image Authentication Under Geometric AttacksImage Authentication Under Geometric Attacks
• Basic premise– Feature points of a reference image and a geometrically
attacked image are related by a suitable transformation – Affine transformation models the geometric distortion
x = (x1, x2) , y = (y1, y2) R – 2 x 2 matrix, t – 2 x 1 vector
• Hausdorff distance to compare feature points from two images [Atallah, 1983; Rote 1991]
– Used in computer vision for locating objects in an image– Relatively insensitive to perturbations in feature points, can
tolerate errors due to occlusion or feature detector failure
Expected Contribution # 3
tRxxy )(A
33
Image Authentication Under Geometric AttacksImage Authentication Under Geometric Attacks
• Hausdorff distance between point sets A and B– A = {a1,…, ap} and B = {b1,…, bq}
where
– Measures degree of mismatch between two sets
• Employ structure matching algorithms [Huttenlocher et al. 1993, Rucklidge 1995]
– To determine G such that
– Here, fr and fc denote feature point sets from reference and candidate image to be authenticated
Expected Contribution # 3
)),(),,(max(),( ABBABA hhH
||||minmax),( bahba
BA
BA
transformaffine ),,(minarg AAoHG crA
ff
34
Conclusion & Future WorkConclusion & Future Work
Conclusion
• Feature point based hashing framework Iterative feature detector that preserves significant image
geometry, features invariant under several attacksTrade-offs facilitated between hash algorithm goals
• Algorithms for feature vector compressionNovel cost function for the hashing application– Heuristic clustering algorithm(s) to minimize this cost– Randomized clustering for secure hashing
• Image authentication under geometric attacksAffine transformation to model geometric distortions– Hausdorff distance and structure matching algorithms to
determine affine transformation and authenticate
35
Proposed ScheduleProposed Schedule
Conclusion
Semester Work Plan
Summer 2004 Perform extensive tests on the feature extraction algorithm, implement the solution
to stage 1Fall 2004 Develop and finalize the clustering
algorithm for feature vector compression. Compare with other approaches viz. error
correction decodingSpring 2005 Finalize the design and implement the
scheme for image authentication under geometric attacks
Summer 2005 Implement the two-step hash algorithm
Fall 2005 Write and defend dissertation
36
Backup Slides
37
• Parsing in compiling a program
• Variable names kept in a data structure– Array of pointers, each pointer points to a linked list
– Index into the array is a hash value
• Example: variable name “university”– Hashing Scheme – Sum of ASCII codes of characters in a
variable name computed modulo N a prime number
– Check linked list at array index, add string to linked list if it had not been previously parsed
Hash: Illustrative ExampleHash: Illustrative ExampleIntroduction
38
End-Stopped Wavelets…ExampleEnd-Stopped Wavelets…Example
•Morlet Wavelet along
the u-axis
•FDoG operator along
frequency axis v
Expected Contribution #1
)4
)2(
4( 00
22
4
1),(
jxkkyx
E yeyx
)
2()
2
)((
22220
2),(ˆvuvku
E jveevu
Synthetic L-shaped image Response of Morlet wavelet, orientation = 0 degrees
Response of the end-stopped wavelet
spatial domain
frequency domain
39
Content Changing ManipulationsContent Changing ManipulationsFeature Detection
Original image
Maliciously manipulated
image
Back
40
• Image Conditioning– All images resized to 512 x 512 via triangular interpolation prior
to feature extraction– Intensity planes of color images were used
• Pixel neighborhood– Circular to detect isotropic features– Radius of 5 pixels
• Iterative Feature Extraction– wavelet scale, i = 3– MaxIter = 20, ρ = 0.001, P = 128– LSI filter: zero-phase low pass filter (11 x 11) designed
using McCllelan transformations– Order statistics filtering: median with 5 x 5 window
Algorithm ParametersAlgorithm ParametersResults
Back
41
Experimental ResultsExperimental ResultsFeature Detection
AWGN
σ = 20
90 degree rotation
42
Trade-offsTrade-offs
Expected Contribution # 1
• Perceptual robustness vs. fragility– Size of the search neighborhood: large feature points are
more robust– Select feature points such that
– T1, T2 large enough implies features retained in several attacked versions of the image else removed easily
• Robustness vs. Randomization– Uptil N < Nmax, robustness largely retained else random
regions shrink to the extent that they do not contain significant chunks of image geometry
21 ),,( max TyxWT i
Back
43
Relation Based Scheme : DCT coefficientsRelation Based Scheme : DCT coefficients
Digital Signature Techniques
• Discrete Cosine Transform (DCT)
– Typically employed on 8 x 8 blocks
• Digital Signature by Lin
– Fp, Fq, DCT coefficients at the same positions in two different 8 x 8 blocks
– , DCT coefficients in the compressed image
00 qpqp FFFF
pF
qF Back
1
0
1
0
2121 )12(
2cos)12(
2cos),(4),(
N
i
N
j
jN
ki
N
kjiIkkB
8 x 8 block
p q N x N image
44
Multi-Resolution ApproximationsMulti-Resolution Approximations
Wavelet Decomposition
45Back
46
Examples of Perceptually Identical Images Examples of Perceptually Identical Images
Wavelet Decomposition
Original Image Contrast EnhancedJPEG, QF = 10
10% cropping 3 degree rotation2 degree rotation
Back
47
Iterative Hash AlgorithmIterative Hash Algorithm
Expected Contribution # 1
Extract Feature Vector Probabilistic Quantization
Order Statistics FilteringLinear Shift Invariant Low
pass filtering
Probabilistic QuantizationExtract Feature Vector
Input Image
D(b1, b2) < ρ
48
Probabilistic QuantizationProbabilistic Quantization
Quantization
• Feature Vector– fmn = m + H*n
• Quantization Scheme– L quantization levels – Design quantization bins [li,li-1) such that
– Quantization Rule
i
i
l
l
f LiL
xp1
1 ,1
)(
iklkl qii )( )(1 ffBack
49
Feature Vector ExtractionFeature Vector ExtractionFeature Detection
• Randomization– Partition the image into N regions using k-means
segmentation – extract feature points from each region
– Secret key K is used to generate initial guesses for the clusters (centroids of random regions)
– Avoid very small regions since they would not yield robust image features
Back
50
Preliminary ResultsPreliminary Results
Attack
Thresholding of coarse wavelet
coefficients (Mihcak et al.)
Proposed feature point
detector
JPEG, QF = 10 0.01 0.04AWGN, σ = 20 0.03 0.04
Gaussian Smoothing 0.00 0.01Median Filtering 0.04 0.02
Scaling 50% 0.02 0.08Rotation 2 degrees 0.09 0.12
Cropping 10% 0.12 0.14Cropping 20% 0.16 0.24* Small object
addition0.17 0.54
* Tamper with facial features
0.14 0.42
Expected Contribution #1
Table 1. Comparison of quantized feature vectors
Normalized Hamming distance between quantized feature vectors of original and attacked images
51
Minimizing the CostMinimizing the Cost
Clustering Algorithms
• Decision Version of the Clustering Problem– For a fixed number of clusters k, is there a clustering with
cost less than a constant? – Shown to be NP-complete via a reduction from the k-way
graph cut problem [Monga et. al, 2004]
• Polynomial time greedy heuristic to solve the problem– Select cluster centers based on probability mass of vectors
in V – minimize error probabilities in a rigorous sense– Trade-offs: Exclusive minimization of would
compromise and vice-versa– Basic algorithm with variations to facilitate trade-offs
1CE
2CE
52
Basic Clustering AlgorithmBasic Clustering Algorithm
Clustering Algorithms
1. Obtain ε, δ, set k = 1. Select the data point associated with the highest probability mass, label it l1
2. Make the first cluster by including all unclustered points lj such that
D(l1, lj) < ε/2
3. k = k + 1. Select the highest probability data point lk amongst the unclustered points such that
where S is any cluster, C – set of clusters formed till this step and
4. Form the kth cluster Sk by including all unclustered points lj such that
D(lk, lj) < ε/2
5. Repeat steps 3-4 till no more clusters can be formed
23),(min
SlD k
CS
),(min),( yxDSxDSy
53 2/
Visualization of the Clustering AlgorithmVisualization of the Clustering Algorithm
Clustering Algorithms
54
ObservationsObservations
Clustering Algorithms
• For any (li, lj) in cluster Sk
• No errors till this stage of the algorithm– Each cluster is atleast ε away from any other cluster and
hence there are no errors by violating (1) – Within each cluster the maximum distance between any
two points is at most ε, and because 0 < ε < δ there are no errors by violation of (2)
– The data points that are left unclustered are atleast 3 ε /2 away from each of the existing clusters
• Next– Two different approaches to handle the unclustered points
),(),(),( jkk
iji llDllDllD
55
Input Image I
Final Hash Value
Hashing FrameworkHashing FrameworkExpected Contribution #1
Compress
Features
• Two-stage Hash algorithm
Feature Vectors extracted from “perceptually identical” images must be close in a distance metric
Extract visually robust feature vector
56
Approach 1Approach 1
Clustering Algorithms
1. Select the data point l* amongst the unclustered data points that has the highest probability mass
2. For each existing cluster Si, i = 1,2,…, k compute
Let S(δ) = {Si such that di ≤ δ}
3. IF S(δ) = {Φ} THEN k = k + 1. Sk = l* is a cluster of its own
ELSE for each Si in S(δ) define
where denotes the complement of Si i.e. all clusters in S(δ) except Si. Then, l* is assigned to the cluster S* = arg min F(Si)
4. Repeat steps 1 through 3 till all data points are exhausted
),(max * xlDdiSx
i
iSl
i llclplpSF ),()()()( *1
*
iS
57
Approach 2Approach 2
Clustering Algorithms
1. Select the data point l* amongst the unclustered data points that has the highest probability mass
2. For each existing cluster Si, i = 1,2,…, k define
and β lies in [1/2, 1]
where denotes the complement of Si i.e. all existing clusters except Si. Then, l* is assigned to the cluster S* = arg min F(Si)
3. Repeat steps 1 and 2 till all data points are exhausted
ii SlSl
i llclplpllclplpSF ),()()()1(),()()()( *2
**1
*
iS
58
SummarySummary
Clustering Algorithms
• Approach 1– Tries to minimize conditioned on = 0
• Approach 2– Smoothly trades off the minimization of vs. via the parameter β– β = ½ joint minimization– β = 1 exclusive minimization of
• Final Hash length determined automatically!– Given by bits, where k is the total number of
clusters formed– Proposed clustering can be used to compress feature
vectors in any metric space e.g. euclidean, hamming
1CE
2CE 1CE
2CE
1CE
k2log
59
Randomized Clustering for Secure HashingRandomized Clustering for Secure Hashing
Clustering Algorithms
• Heuristic for the deterministic map – Select the highest probability data point amongst the unclustered data
points
• Randomization Scheme– Normalize the probabilities of the existing unclustered data points to
define a new probability mass such that
where i runs over unclustered points, – Employ a uniformly distributed random variable in [0,1]
(generated via a secret key) to select the data point i as a cluster center with probability
i
si
sis
i p
p)(
)(si
)(si
s
60
Randomized Clustering: IllustrationRandomized Clustering: Illustration
Clustering Algorithms
• Example: s = 1– 4 data points with probabilities 0.5, 0.25, 0.125, 0.125
• Key Observations– s = 0, is uniform or any point is selected as the
cluster center with the same probability– s = deterministic clustering
1 2 3 40
0.5 0.75
0.8751
Uniform number
generation to select data
point
)0(i
otherwise 0
point prob.highest for the 1)(i
61
Clustering: ResultsClustering: Results
Clustering Algorithms
• Compress binary feature vector of L = 240 bits– Final hash length = 46 bits, with Approach 2, β = 1/2
• *Average distortion VQ at the same rate– Value of cost function is orders of magnitude lower for the
proposed clustering
Clustering Algorithm
Approach 1 7.64 * 10-8 0
Approach 2, β = ½ 7.43 * 10-9 7.464 * 10-10
Approach 2, β = 1 7.17 * 10-9 4.87 * 10-9
*Average distance VQ 5.96 * 10-4 3.65 * 10-5
1CE 2CE
62
Conclusion & Future WorkConclusion & Future Work
Clustering Algorithms
• Perceptual Image Hashing via Feature Points– Extract Feature Points that preserve significant image geomtery– Based on properties of the Human Visual System (HVS)– Robust to local and global geometric distortions
• Clustering Algorithms for compression– Randomized to minimize vulnerability against malicious attacks
generated by an adversary– Trade-offs facilitated between robustness and randomness,
fragility
• Future Work– Authentication under geometric attacks– Information theoretically secure hashing
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• Feature Points are required to be invariant across “perceptually identical” images– Primary geometric features of the image are largely
preserved under small perturbations [Mihcak et. al, 2001]
– i.e. extract significant image geometry preserving feature points
– Identify what the human eye perceives as “robust” or “invariant” geometric features
• Edge based detection is not suited– Has problems with high compression ratios, quantization
and scaling [Zheng and Chellapa, 1993]
– Human recognition performance does not impede even when much edge information is lost [Beiderman, 1987]
Perceptual Image Hashing Via Feature PointsPerceptual Image Hashing Via Feature PointsImage Hashing Via Feature Points
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ES2 WaveletES2 WaveletEnd-stopping and image features
• Example Wavelets– SDoG operator on the morlet wavelet
• Wavelet behavior– produces a strong response at the center of any oriented
linear stimuli of a particular length determined by σ
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Clustering: Dependence on source distributionClustering: Dependence on source distribution
Clustering Algorithms
• Source distributions may be very “skewed”– Trivial clusters may be formed i.e. with very low probability
points included– For efficient compression, the number of clusters formed
should accurately represent the statistics of the source
• Solution– Consider the algorithm when m clusters are formed m < k and
i < n points already clustered– Assign remaining points i.e. {i + 1, …, n} to the remaining
clusters in a fashion similar to the basic algorithm– Compare the expected cost of this clustering vs. the one with
k clusters as formed by the algorithm described before, if the increase is not significant terminate with the current number of clusters