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http://signal.ece.utexas.edu. 2004 IEEE Int. Conference on Image Processing. Robust Perceptual Image Hashing Using Feature Points. Vishal Monga and Prof. Brian L. Evans. October 25 th , 2004. Embedded Signal Processing Laboratory The University of Texas at Austin Austin, TX 78712-1084 USA
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http://signal.ece.utexas.edu 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 LaboratoryThe University of Texas at AustinAustin, TX 78712-1084 USA {vishal, bevans}@ece.utexas.edu
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 representationof large digital message • Example: sum of ASCII codes forcharacters in name modulo N (= 7),a prime number Database name search example
Introduction Perceptual Hash: Desirable Properties • Perceptual robustness • Fragility to distinct inputs • Unpredictability • Necessary in security applications to minimize vulnerability against malicious attacks
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 Tampered JPEG Compressed Original Image Different hash values Same hash value h1 h2
Related Work Content Based Digital Signatures • Extract signature from intensity statistics • Intensity histogramsof 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]
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 Final Hash Compression
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
Morlet End-stopped L-shaped image 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
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
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
Feature Extraction 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 produceIos = OS(I;p,q,r) 3. Preserve “strong” image geometry: Perform low-pass linear shift invariant (LSI) filtering on Iosto obtain Ilp 4. Repeat step 1 with Ilpto obtainbf2 5. IF (count = MaxIter) go to step 6. ELSE IF D(bf1, bf2) < ρ go to step 6. ELSE set I = Ilpand 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
Feature Extraction Image Features at Algorithm Convergence Original Image JPEG, QF = 10 AWGN, σ = 10 Stirmark local geometric attack
Feature Extraction Results: Feature Extraction • Feature Vector Comparison D(fv(I), fv(Iident)) < 0.2 D(fv(I), fv(Idiff)) > 0.3 Table 1. Comparison of feature vectors Normalized Hamming distance between feature vectors of original and attacked images *Attacked images generated by Stirmark benchmark software
Feature Extraction Results: Feature Extraction YES survives attack, i.e. hash was invariant *content changing manipulations, should be detected
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, T2large 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
Conclusion 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?
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
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
Back Wavelet Decomposition Examples of Perceptually Identical Images Original Image JPEG, QF = 10 Contrast Enhanced 10% cropping 2 degree rotation 3 degree rotation
Feature Detection Back Content Changing Manipulations Original image Maliciously manipulated image
Outline 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