Run-length and edge statistics based approach for image splicing detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5450 LNCS, Issue , 2009, Pages 76-87

  Dong, Jing ^a   Wang, Wei ^a   Tan, Tieniu ^a   Shi, Yun Q ^b  

^a INSTITUTE OF AUTOMATION   (China)

^b NEW JERSEY INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Characteristic functions; Edge detection; Image splicing; Run length; Support vector machine (SVM)

Indexed keywords

CHARACTERISTIC FUNCTIONS; DETECTION ACCURACY; FUNDAMENTAL OPERATIONS; IMAGE CHARACTERISTICS; IMAGE EDGE; IMAGE FORENSICS; IMAGE FORGERY; IMAGE PIXELS; IMAGE SPLICING; IMAGE SPLICING DETECTION; PASSIVE DETECTION; PATTERN RECOGNITION PROBLEMS; RUN LENGTH; STATISTICAL FEATURES; SUPPORT VECTOR MACHINE (SVM);

COMPUTATIONAL COMPLEXITY; DIGITAL WATERMARKING; EDGE DETECTION; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 78650323484     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04438-0_7     Document Type: Conference Paper

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