@Article{cmes.2020.010869,
AUTHOR = {S. Velliangiri, J. Premalatha},
TITLE = {A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {125},
YEAR = {2020},
NUMBER = {2},
PAGES = {625--645},
URL = {http://www.techscience.com/CMES/v125n2/40312},
ISSN = {1526-1506},
ABSTRACT = {Different devices in the recent era generated a vast amount of
digital video. Generally, it has been seen in recent years that people are
forging the video to use it as proof of evidence in the court of justice.
Many kinds of researches on forensic detection have been presented, and
it provides less accuracy. This paper proposed a novel forgery detection
technique in image frames of the videos using enhanced Convolutional Neural Network (CNN). In the initial stage, the input video is taken as of
the dataset and then converts the videos into image frames. Next, perform
pre-sampling using the Adaptive Rood Pattern Search (ARPS) algorithm
intended for reducing the useless frames. In the next stage, perform preprocessing for enhancing the image frames. Then, face detection is done
as of the image utilizing the Viola–Jones algorithm. Finally, the improved
Crow Search Algorithm (ICSA) has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network (ECNN)
classifier for detecting the forged image frames. The experimental outcome
of the proposed system has achieved 97.21% accuracy compared to other
existing methods.},
DOI = {10.32604/cmes.2020.010869}
}