BULGARIAN ACADEMY OF SCIENCES

Abstract Due to the availability of a large number of image editing software, it is very easy to find duplicate copies of original images. In such a situation, there is a need to develop a robust technique that can be used for the identification of duplicate copies apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on uniform Local Binary Pattern (LBP). Here, the input image is initially pre-processed before calculating the Local Binary Pattern (LBP) which is used for image identification. Experiments show that proposed hashing gives excellent performance against the Histogram equalization attack. The Receiver Operating Curve (ROC) indicates that the proposed hashing also performs better in terms of robustness and discrimination. Support Vector Machine (SVM) classifier shows that generated features can easily classify images into a set of similar and different images, and can classify new data with a high level of accuracy.

[1]  Shiguo Lian,et al.  A passive image authentication scheme for detecting region-duplication forgery with rotation , 2011, J. Netw. Comput. Appl..

[2]  Ram Kumar Karsh,et al.  Robust image hashing using ring partition-PGNMF and local features , 2016, SpringerPlus.

[3]  Zhenjun Tang,et al.  Robust image hash function using local color features , 2013 .

[4]  Shang-Lin Hsieh,et al.  Using binarization and hashing for efficient SIFT matching , 2015, J. Vis. Commun. Image Represent..

[5]  Zhenjun Tang,et al.  Robust Image Hashing With Singular Values Of Quaternion SVD , 2019 .

[6]  Mohamed Deriche,et al.  A bibliography of pixel-based blind image forgery detection techniques , 2015, Signal Process. Image Commun..

[7]  Huazhong Shu,et al.  Robust hashing for image authentication using quaternion discrete Fourier transform and log-polar transform , 2015, Digit. Signal Process..

[8]  Fang Liu,et al.  Wave atom transform generated strong image hashing scheme , 2012 .

[9]  Zhenjun Tang,et al.  Video Hashing with DCT and NMF , 2020, Comput. J..

[10]  Giovanni Maria Farinella,et al.  Robust Image Alignment for Tampering Detection , 2012, IEEE Transactions on Information Forensics and Security.

[11]  Jiuchao Feng,et al.  Robust image hashing using invariants of Tchebichef moments , 2014 .

[12]  Zhenjun Tang,et al.  Robust image hashing based on color vector angle and Canny operator , 2016 .

[13]  K. Rhee,et al.  A key-dependent secure image hashing scheme by using Radon transform , 2009, 2009 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS).

[14]  Aditi,et al.  Robust image hashing through DWT-SVD and spectral residual method , 2017, EURASIP Journal on Image and Video Processing.

[15]  Zhenjun Tang,et al.  Robust image hashing with dominant DCT coefficients , 2014 .

[16]  Fan Yang,et al.  Robust image hashing via colour vector angles and discrete wavelet transform , 2014, IET Image Process..

[17]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[18]  Zhenjun Tang,et al.  Robust image hashing with visual attention model and invariant moments , 2020, IET Image Process..

[19]  Xinpeng Zhang,et al.  Robust Image Hashing for Tamper Detection Using Non-Negative Matrix Factorization , 2008 .

[20]  Fouad Khelifi,et al.  Perceptual Image Hashing Based on Virtual Watermark Detection , 2010, IEEE Transactions on Image Processing.