Robust Image Hashing Based on Color Vector Angle and Canny Operator Source: AEU-International Journal of Electronics and Communications (2016) Authors: Zhenjun Tang, Liyan Huang, Xianquan Zhang, Huan Lao Speaker: Meihui Sun Date: 2016-12-1

Outline Introduction Proposed Scheme Experimental Results Conclusions - Color vector angle calculation - Edge detection - Statistical feature extraction Experimental Results - Perceptual robustness - Discriminative capability - Effect of circle number - Performance comparisons - Reference Conclusions

Introduction Application: image forensics, retrieval, index Convention image hashing algorithms have limitation in robustness and discrimination. This hashing robust against normal digital processing.

Proposed Scheme(1/9) - Frame This image hashing consists of three steps.

Proposed scheme(2/9) - Preprocessing Original Image 512*512 Image Processed Image resized blurred The convolution mask of Gaussian low-pass filter: Where is the standard deviation of all elements in the convolution mask. 卷积模板中所有元素的标准差

Proposed scheme(3/9) - Calculation of color vector angle Luminance Features of a color image: Hue Saturation Comparing with RGB space:

Proposed scheme(4/9) - Calculation of color vector angle The color vector angle θ can be calculated by: To reduce computational cost:

Proposed scheme(5/9) - Calculation of color vector angle Reference color Pref = [Rref， Gref， Bref] : Ri,j, Gi,j and Bi,j are the red, green and blue components of the image pixel Pi,j in the i-th row and the j-th column (1 ≤ i ≤ M, 1 ≤ j ≤ M).

Proposed scheme(6/9) - Calculation of color vector angle Matrix Acolor(the color vector angle sinθi,j between Pi,j and Pref ): Conversion from the color image to color vector angle:

Proposed scheme(7/9) - Edge detection Steps: 1.Convert the preprocessed image to YCbCr color space; 2.Take the luminance component for representation; 3.Apply the Canny operator to the luminance component. 1, Pi,j is an edge point. Ei,j= 0, Pi,j is not an edge point.

Proposed scheme(8/9) - Statistical feature extraction K --- the number of circles. rk --- the k-th radius labeled (r1and rk are the innermost and outermost radii). di,j --- the distance from the pixel Pi,j(1≤i≤M, 1≤j≤M) to the image center (xc,yc). d M

Proposed scheme(9/9) - Statistical feature extraction E(k)--- the set of color vector angle of those edge pixels on the k-th concentric circle Δd : a pre-defined threshold for error control. Nk : the element number of E(k) Quantized: Hash h: Consequently, E(k) can be determined by the following equation: The variance vk:

Experimental Results (1/8) - Similarity evaluation u1 and u2 : the means of h1 and h2. h1(k) and h2(k) : the k-th elements of h1 and h2. A threshold T: S＞T similar images S≤T different image Use the correlation coefficient to measure similarity between two image hashes

Experimental Results (2/8) - Perceptual robustness 42 color images: Including Airplane, Baboon, House, Peppers, and Lena and 37 images (all color images) of the USC-SIPI Image Database.

Experimental Results (3/8) - Perceptual robustness

Experimental Results (4/8) - Discriminative capability 200 different color image:

Experimental Results (5/8) - Discriminative capability 200*199/2=19000. Minimum:-0.86918 Maximum:0.97059 Mean:0.17532 Standard deviation:0.35747 There are, pairs of different images If T=0.95,there are only 0.04% different image wrongly considered as similar images.

Experimental Results (6/8) - Effect of circle number Receiver operating characteristics (ROC): True positive rate : False positive rate: K=40

Experimental Results (7/8) - Performance comparisons

Experimental Results (8/8) - Performance comparisons [1]Tang Z, Dai Y, Zhang X, Zhang S. Perceptual image hashing with histogram of color vector angles. In: The 8th international conference on active media technology (AMT 2012). 2012. p. 237–46. [2]Li Y, Lu Z, Zhu C, Niu X. Robust image hashing based on random Gabor filtering and dithered lattice vector quantization. IEEE Trans Image Process. [3]Ou Y, Rhee KH. A key-dependent secure image hashing scheme by using Radon transform. In: Proceedings of the IEEE international symposium on Intelligent signal processing and communication systems. 2009. p. 595–8. [4]Laradji IH, Ghouti L, Khiari E-H. Perceptual hashing of color images using hyper-complex representations. In: Proceedings of the IEEE international conference on image processing (ICIP 2013). 2013. p. 4402–6.

Conclusions 1.This HASH is robust against normal content-preserving manipulations. 2.This hash has a good performances in robustness and discriminative capability.

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