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001444269 001__ 1444269
001444269 003__ OCoLC
001444269 005__ 20230310003701.0
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001444269 020__ $$a9789811690419$$q(electronic bk.)
001444269 020__ $$a9811690413$$q(electronic bk.)
001444269 020__ $$z9789811690402
001444269 020__ $$z9811690405
001444269 0247_ $$a10.1007/978-981-16-9041-9$$2doi
001444269 035__ $$aSP(OCoLC)1295578327
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001444269 049__ $$aISEA
001444269 050_4 $$aTA1637$$b.S66 2022
001444269 08204 $$a621.36/7$$223
001444269 1001_ $$aSoni, Badal,$$eauthor.
001444269 24510 $$aImage copy-move forgery detection :$$bnew tools and techniques /$$cBadal Soni, Pradip K. Das.
001444269 264_1 $$aSingapore :$$bSpringer,$$c[2022]
001444269 264_4 $$c©2022
001444269 300__ $$a1 online resource :$$billustrations (chiefly color).
001444269 336__ $$atext$$btxt$$2rdacontent
001444269 337__ $$acomputer$$bc$$2rdamedia
001444269 338__ $$aonline resource$$bcr$$2rdacarrier
001444269 347__ $$atext file$$bPDF$$2rda
001444269 4901_ $$aStudies in computational intelligence ;$$vvolume 1017
001444269 504__ $$aIncludes bibliographical references.
001444269 5050_ $$aIntroduction -- Background Study and Analysis -- Copy-Move Forgery Detection using Local Binary Pattern Histogram Fourier Features -- Blur Invariant Block-based CMFD System using FWHT Features -- Geometric Transformation Invariant Improved Block based Copy-Move Forgery Detection -- Key-points based Enhanced Copy-Move Forgery Detection System using DBSCAN Clustering Algorithm -- Image Copy-Move Forgery Detection using Deep Convolutional Neural Networks.
001444269 506__ $$aAccess limited to authorized users.
001444269 520__ $$aThis book presents a detailed study of key points and block-based copy-move forgery detection techniques with a critical discussion about their pros and cons. It also highlights the directions for further development in image forgery detection. The book includes various publicly available standard image copy-move forgery datasets that are experimentally analyzed and presented with complete descriptions. Five different image copy-move forgery detection techniques are implemented to overcome the limitations of existing copy-move forgery detection techniques. The key focus of work is to reduce the computational time without adversely affecting the efficiency of these techniques. In addition, these techniques are also robust to geometric transformation attacks like rotation, scaling, or both.
001444269 588__ $$aDescription based on print version record.
001444269 650_0 $$aImage processing$$xDigital techniques.
001444269 650_0 $$aDigital images$$xForgeries.
001444269 650_0 $$aDigital forensic science.
001444269 650_6 $$aTraitement d'images$$xTechniques numériques.
001444269 650_6 $$aImages numériques$$xFaux.
001444269 655_0 $$aElectronic books.
001444269 7001_ $$aDas, Pradip K.$$eauthor.
001444269 77608 $$iPrint version:$$aSONI, BADAL. DAS, PRADIP K.$$tIMAGE COPY-MOVE FORGERY DETECTION.$$d[Place of publication not identified] : SPRINGER VERLAG, SINGAPOR, 2022$$z9811690405$$w(OCoLC)1286796166
001444269 830_0 $$aStudies in computational intelligence ;$$vv. 1017.
001444269 852__ $$bebk
001444269 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-9041-9$$zOnline Access$$91397441.1
001444269 909CO $$ooai:library.usi.edu:1444269$$pGLOBAL_SET
001444269 980__ $$aBIB
001444269 980__ $$aEBOOK
001444269 982__ $$aEbook
001444269 983__ $$aOnline
001444269 994__ $$a92$$bISE