1. Vishal Monga and Prof. Brian L. Evans
2004 IEEE Int. Conference on Image Processing
Robust Perceptual Image Hashing Using Feature Points
Vishal Monga and Prof. Brian L. Evans
October 25th , 2004
Embedded Signal Processing Laboratory The University of Texas at Austin Austin, TX USA
{vishal,

2. Database name search example
Introduction
Hash Example
Hash function: Projects value from set with large (possibly infinite) number of members to set with fixed number of (fewer) members
Irreversible
Provides short, simple representation of large digital message
Example: sum of ASCII codes for characters in name modulo N (= 7), a prime number
Name
Hash Value
Ghosh 1
Monga 2
Baldick 3
Vishwanath
Evans 5
Geisler
Gilbert 6
Database name search example

3. Perceptual Hash: Desirable Properties
Introduction
Perceptual Hash: Desirable Properties
Perceptual robustness
Fragility to distinct inputs
Unpredictability
Necessary in security applications
to minimize vulnerability against
malicious attacks
Symbol
Meaning
H(I)
Hash value extracted from image I
Iident
Image identical in appearance to I
Idiff
Image clearly distinct in appearance w.r.t I m
Length of hash (in bits)

4. Image Hashing: Motivation
Introduction
Image 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
JPEG Compressed
Original Image
Tampered
Different hash values
Same hash value h1 h2

5. Content Based Digital Signatures
Related Work
Content Based Digital Signatures
Extract signature from intensity statistics
Intensity histograms of image blocks [Schneider et al., 1996]
Mean, variance and kurtosis of intensity values extracted from image blocks [Kailasanathan et al., 2001]
Preserve coarse representations
Threshold low frequency DCT coefficients [Fridrich et al., 2001]
Low-res wavelet sub-bands [Mihcak & Venkatesan, 2000, 2001]
Relation based methods
Invariant relationship between corresponding DCT coefficients in two 8  8 blocks [Lin & Chang, 2001]
Interscale relationship of wavelet coefficients [Lu & Liao, 2003]

6. Perceptual Image Hashing
Hashing Framework
Perceptual Image Hashing
Two-stage hash algorithm
Feature vectors extracted from “perceptually identical” images must be close in distance metric
Input Image I
Compression
Final Hash

7. Hypercomplex or End-Stopped Cells
End-stopping and curvature
Hypercomplex or End-Stopped Cells
Cells in 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]
Develop filters/kernels that capture this behavior
Robustness to changes in image resolution
Use wavelet based approach
“End-stopping and Image Geometry”, Dobbins, 1989

8. End-Stopped Wavelets [Vandergheynst et al., 2000]
End-stopped wavelet basis
Apply First Derivative of Gaussian (FDoG) operator to detect end-points of structures identified by Morlet wavelet
Morlet wavelet along u frequency axis detects vertically oriented linear structures
FDoG operator along v frequency axis applied on Morlet wavelet to detect end-points and corners
L-shaped image
Morlet
End-stopped

9. Computing Wavelet Transform
Feature Extraction
Computing Wavelet Transform
Generalize end-stopped wavelet
Employ 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
End-stopped wavelet transform

10. Proposed Feature Detection Method
Feature Extraction
Proposed Feature Detection Method
Compute wavelet transform of image I at suitably chosen scale i for several different orientations
Significant feature selection: Locations (x,y) in the image that are identified as candidate feature points satisfy
Avoid trivial (and fragile) features: Qualify a location as a final feature point if
Randomization: Partition the image into N random regions using a secret key K, extract features from each random region
Perceptual Quantization: Quantize features based on distribution (histogram) to enhance robustness

11. Iterative Feature Extraction Algorithm
Extract feature vector f of length P from image I, quantize f perceptually to obtain a binary string bf1 (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, bf2) < ρ go to step 6.
ELSE set I = Ilp and go to step 1.
6. Set fv(I) = bf2
MaxIter, ρ, P, and count are algorithm parameters.
* count = 0 to begin with
fv(I) denotes quantized feature vector
D(.,.) – normalized Hamming distance between its arguments

12. Image Features at Algorithm Convergence
Feature Extraction
Image Features at Algorithm Convergence
Original Image
JPEG, QF = 10
AWGN, σ = 10
Stirmark local geometric attack

13. Results: Feature Extraction
Feature Vector Comparison
D(fv(I), fv(Iident)) < 0.2
D(fv(I), fv(Idiff)) > 0.3
*Attack
Lena
Bridge
Peppers
JPEG, QF = 10
0.04
0.06
AWGN, σ = 20
0.03
0.02
Contrast Enhancement
Gaussian Smoothing
0.01
0.05
Median Filtering
0.07
Scaling by 50%
0.08
0.14
0.11
Rotation by 20
0.12
0.15
Rotation by 50
0.18
0.20
0.19
Cropping by 10%
0.13
Cropping by 20%
0.21
0.22
0.24
Table 1. Comparison of feature vectors
Normalized Hamming distance between feature vectors of original and attacked images
*Attacked images generated by Stirmark benchmark software

14. Proposed feature point detector
Feature Extraction
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
AWGN, σ = 20
Gaussian Smoothing
Median Filtering NO
Scaling 50%
Rotation 2 degrees
Cropping 10%
Cropping 20%
* Small object addition
* Tamper with facial features
YES  survives attack, i.e. hash was invariant
*content changing manipulations, should be detected

15. Highlights Invariant feature extraction Trade-offs facilitated
Summary
Highlights
Invariant feature extraction
Wavelet kernels based on cells in visual cortex
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 regions
Until N < Nmax, robustness largely preserved else random regions shrink to the extent that they do not contain significant chunks of image geometry

16. Questions and Comments!

17. Back up slides

18. Conclusion & Future Work
Decouple image hashing into
Feature extraction and data clustering
Feature point based hashing framework
Iterative feature detector that preserves significant image geometry, features invariant under several attacks
Trade-offs facilitated between hash algorithm goals
Clustering of image features [Monga, Banerjee & Evans, 2004]
Randomized clustering for secure image hashing
Future Work
Hashing under severe geometric attacks
Provably secure image hashing?

19. End-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
x – (x,y) 2-D spatial co-ordinates
ko – (k0, k1) wave-vector of the mother wavelet
Orientation control –
Back

20. Feature Vector Extraction
Feature Detection
Feature Vector Extraction
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

21. Examples of Perceptually Identical Images
Back
Wavelet Decomposition
Examples of Perceptually Identical Images
Original Image
JPEG, QF = 10
Contrast Enhanced
10% cropping
2 degree rotation
3 degree rotation

22. Content Changing Manipulations
Feature Detection
Back
Content Changing Manipulations
Original image
Maliciously manipulated image

23. Outline Introduction: Motivation and applications
Summary of digital signature methods
Image statistics based approaches
Relation based schemes (DCT/Wavelet)
Perceptual hashing via image feature points
Two stage hash algorithm: Feature extraction + clustering
End stopped wavelets for feature detection
Iterative scheme for feature extraction based on preserving significant image geometry
Experimental Results
Incidental and malicious attacks
Conclusion & Future Work