caa a22 4500 445851465 CHVBK 20180317145414.0 cr unu---uuuuu 170323e20111001xx s 000 0 eng 10.1007/s11227-010-0531-y doi (NATIONALLICENCE)springer-10.1007/s11227-010-0531-y An improved image blind identification based oninconsistency in light source direction [Elektronische Daten] [Yingda Lv, Xuanjing Shen, Haipeng Chen] In this study, an improved image blind identification algorithm based on inconsistency in light source direction was proposed. And a new method defined as "neighborhood method” was presented, which was used to calculate surface normal matrix of image in the blind identification algorithm. For an image, there is an error function between its actual light intensity and calculated light intensity, and for different light source models, there are different constraint functions of light. Light source direction which makes both error function and constraint function get the minimum is the one we want to seek. On this basis, according to the error function and the corresponding constraint function, search means and the Hestenes-Powell multiplier method were used in the improved algorithm to calculate the light source direction for local and infinite light source images, respectively. Further, the authenticity of image can be determined by the inconsistency in light source direction of different areas in the image. Experimental results showed that the light source direction of different areas in an image could be calculated accurately, and then the image tampering can be detected effectively by the improved algorithm. Moreover, the performance of the improved algorithm of the proposed blind identification is superior to that of the existing one in terms of detection rate and time complexity. Springer Science+Business Media, LLC, 2010 Image tampering nationallicence Blind identification nationallicence Search means nationallicence Hestenes-Powell multiplier method nationallicence Neighborhood method nationallicence Light source direction nationallicence Lv Yingda College of Computer Science and Technology, Jilin University, Changchun, China aut Shen Xuanjing College of Computer Science and Technology, Jilin University, Changchun, China aut Chen Haipeng College of Computer Science and Technology, Jilin University, Changchun, China aut The Journal of Supercomputing Springer US; http://www.springer-ny.com 58/1(2011-10-01), 50-67 0920-8542 58:1<50 2011 58 11227 https://doi.org/10.1007/s11227-010-0531-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11227-010-0531-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lv Yingda College of Computer Science and Technology, Jilin University, Changchun, China aut NATIONALLICENCE 700 1- Shen Xuanjing College of Computer Science and Technology, Jilin University, Changchun, China aut NATIONALLICENCE 700 1- Chen Haipeng College of Computer Science and Technology, Jilin University, Changchun, China aut NATIONALLICENCE 773 0- The Journal of Supercomputing Springer US; http://www.springer-ny.com 58/1(2011-10-01), 50-67 0920-8542 58:1<50 2011 58 11227 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer