Tamper video detection and localization using an adaptive segmentation and deep network technique |
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| Affiliation: | 1. Electronics and Communication Engineering, Jawaharlal Nehru Technological University, Anantapuramu, India;2. Electronics and Communication Engineering, NBKR Institute of Science and Technology, Andhra Pradesh, India;1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China;2. Institute of Computing Innovation, Zhejiang University, Hangzhou, China;3. State Key Lab. of Digital Manufacturing Equipment & Technology, Huazhong University of Science & Technology, Wuhan, China;1. Digital Media Technology Lab, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, United Kingdom;2. Department of Computer Science and Engineering, Kyung Hee University, South Korea;3. School of Technology, Environments & Design, University of Tasmania, Australia;1. School of Automation, Guangdong University of Petrochemical Technology, Maoming, China;2. Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong;1. Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan;2. Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK;3. Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan |
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| Abstract: | In this work we have explored the hybrid deep learning architecture for recognizing the tampering from the videos. This hybrid architecture explores the features from the authentic videos to categorize the tampered portions from the forged videos. Initially, the process begins by compressing the input video using the Discrete cosine transform (DCT) based double compression approach. Then, the filtering process is carried out to improve the quality of compressed frame using the bilateral filtering. Then, the modified segmentation approach is applied to segment the frames into different regions. The features from these segmented portions are extracted and fed into hybrid DNN-AGSO (deep neural network- Adaptivf RELATED WORKSe Galactic Swarm Optimization) using Gabor wavelet transform (GWT) technique. Three different datasets are used to evaluate the overall performance they are, VTD, MFC-18, and VIRAT by MATLAB platform. The recognition rate achieved by VTD, MFC-18, and VIRAT datasets are 96%, 95.2%, and 93.47% respectively. |
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| Keywords: | Video forgery Double compression Modified Brain storm optimization (MBSO) Adaptive galactic Swarm optimization (AGSO) Deep Neural Network (DNN) |
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