Detection of image seam carving by using weber local descriptor and local binary patterns

Piva, 2013, An overview on image forensics, ISRN Signal Process, 1, 10.1155/2013/496701 Avidan, 2011, Seam carving for content-aware resizing, ACM Trans Graph, 26, 10, 10.1145/1276377.1276390 Lu, 2011, Seam carving estimation using forensic hash, 9 Sarkar A., Nataraj L., Manjunath B.S.. Detection of seam carving and localization of seam insertions in digital images. Proceedings of the 11th ACM workshop on multimedia and security. ACM; 2009, 107–116. Fillion C, Sharma G. Detecting content adaptive scaling of images for forensic applications. SPIE electronic imaging, International Society for Optics and Photonics 2010;75410Z:112. Wei, 2014, A patch analysis method to detect seam carved images, Pattern Recognit Lett, 36, 100, 10.1016/j.patrec.2013.09.026 Ryu, 2014, Detecting trace of seam carving for forensic analysis, IEICE Trans Inf Syst, 97, 1304, 10.1587/transinf.E97.D.1304 Yin, 2015, Detecting seam carving based image resizing using local binary patterns, Comput Secur, 55, 130, 10.1016/j.cose.2015.09.003 Hong, 2014, Combining LBP difference and feature correlation for texture description, IEEE Trans Image Process, 23, 2557, 10.1109/TIP.2014.2316640 Chen, 2010, WLD: a robust local image descriptor, IEEE Trans Pattern Anal Mach Intell, 32, 1705, 10.1109/TPAMI.2009.155 Hsu, 2014, Objective quality assessment for image retargeting based on perceptual geometric distortion and information loss, IEEE J. Sel. Top. Signal Process., 8, 377, 10.1109/JSTSP.2014.2311884 Fang, 2014, Objective quality assessment for image retargeting based on structural similarity, IEEE J. Emerging Sel. Top. Circuits Syst., 4, 95, 10.1109/JETCAS.2014.2298919 Ma, 2012, Image retargeting quality assessment: a study of subjective scores and objective metrics, IEEE J. Sel. Top. Signal Process., 6, 626, 10.1109/JSTSP.2012.2211996 Muhammad, 2012, Race classification from face images using local descriptors, Int. J. Artif. Intell. Tools, 21, 1250019, 10.1142/S0218213012500194 Ullah, 2014, Gender classification from facial images using texture descriptors, J. Internet Technol., 15, 801 Huan, 2010, Feature selection: an ever evolving frontier in data mining, 4 Yvan, 2007, A review of feature selection techniques in bioinformatics, Bioinformatics, 23, 2507, 10.1093/bioinformatics/btm344 Chang, 2011, LIBSVM: a library for support vector machines, ACM Trans Intell Syst Technol, 2, 27, 10.1145/1961189.1961199 Schaefer, 2003, 472 LIBSVM: a library for support vector machine. http://www.csie.ntu.edu.tw/~cjlin/libsvm/. Li, 2015, Segmentation-based image copy-move forgery detection scheme, IEEE Trans Inf Forensics Secur, 10, 507, 10.1109/TIFS.2014.2381872 Zhou, 2017, Effective and efficient global context verification for image copy detection, IEEE Trans Inf Forensics Secur, 12, 48, 10.1109/TIFS.2016.2601065 Zhou, 2016, Effective and efficient image copy detection with resistance to arbitrary rotation, IEICE Trans Inf Syst, E99-D, 1531, 10.1587/transinf.2015EDP7341 Wang J., Li T., Shi Y.Q., et al. Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl 2016; DOI:10.1007/s11042-016-4153-0. Gu, 2016, Structural minimax probability machine, IEEE Trans Neural Netw Learn Syst, 28, 1646, 10.1109/TNNLS.2016.2544779 Gu, 2017, A robust regularization path algorithm for v-support vector classification, IEEE Trans Neural Netw Learn Syst, 28, 1241, 10.1109/TNNLS.2016.2527796 Gu, 2015, Incremental support vector learning for ordinal regression, IEEE Trans Neural Netw Learn Syst, 26, 1403, 10.1109/TNNLS.2014.2342533