Vol.125, No.2, 2020, pp.625-645, doi:10.32604/cmes.2020.010869
A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images
  • S. Velliangiri1,*, J. Premalatha2
1 CMR Institute of Technology, Hyderabad, 501401, India
2 Kongu Engineering College, Erode, 638052, India
* Corresponding Author: S. Velliangiri. Email: velliangiris@gmail.com
(This article belongs to this Special Issue: Security Enhancement of Image Recognition System in IoT based Smart Cities)
Received 02 April 2020; Accepted 14 June 2020; Issue published 12 October 2020
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.
Adaptive Rood Pattern Search (ARPS); Improved Crow Search Algorithm (ICSA); Enhanced Convolutional Neural Network (ECNN); Viola Jones algorithm Speeded Up Robust Feature (SURF)
Cite This Article
Velliangiri, S., Premalatha, J. (2020). A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images. CMES-Computer Modeling in Engineering & Sciences, 125(2), 625–645.
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