Raven finch optimized deep convolutional neural network model for intra‐frame video forgery detection. (21st November 2022)

• Record Type: Journal Article
• Authors: Singla, Neetu; Singh, Jyotsna; Nagpal, Sushama
• Is Part Of: Concurrency and computation. Volume 35:Number 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-21
• Subjects: deep‐convolutional neural network -- intra‐frame -- optimization -- video forensics -- video forgery; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7516
• Languages: English
• ISSNs: 1532-0626

Abstract

SUMMARY: Due to the tremendous growth of video editing software, it has become extremely simple to introduce malicious content by manipulating multimedia data. This may include modification of videos either by adding or deleting selective frames with malicious intentions. Hence, it is essential to find the forged frames of the videos by introducing efficient and reliable video forensic methods. This article presents an automatic intra‐frame video forgery detection strategy based on a hybrid optimization tuned deep‐convolutional neural network (deep‐CNN) classifier. The significance of the proposed method lies in developing the proposed raven‐finch optimization algorithm that tunes the weights of the deep‐CNN to exhibit enhanced detection accuracy. The proposed raven‐finch optimization algorithm combines raven search agents and finches search agents, possessing the benefits of both search agents. The features of the input video frames act as the input to the deep‐CNN classifier that detects forgery. The performance of the proposed raven‐finch‐based deep CNN method is analyzed in terms of the performance indices, such as accuracy, sensitivity, and specificity. It is attained to be 97.56%, 95.48%, and 96.38%, respectively, which shows the superiority of the proposed method for intra‐frame forgery detection.