Cited By
View all- Panchal HShah H(2023)Multiple forgery detection in digital video based on inconsistency in video quality assessment attributesMultimedia Systems10.1007/s00530-023-01123-929:4(2439-2454)Online publication date: 9-Jun-2023
Video Inter-frame Tampering Detection Based on SN-VGG+BiLSTM-AE Composite Model
Authors: 
Qi Xing, Yun Luo, Zhaoyuan Zhang, Fan ZhangAuthors Info & Claims
Qi Xing, Yun Luo, Zhaoyuan Zhang, Fan ZhangAuthors Info & ClaimsPages 80 - 87
Abstract
Video inter-frame tampering detection is the most common type of forensics in video forensics. The traditional detection method is to detect tampering by extracting digital image features of video frames, such as SIFT, HOG, and ORB. The accuracy of frame discrimination and localization is limited. This paper introduces deep learning into the problem of tampering detection, and proposes a composite network model structure using the Siamese network Siamese and the bidirectional long short-term memory network autoencoder BiLSTM AutoEncoder to detect tampered frames. Among them, Siamese calculates the inter-frame distance by calculating the depth features of the frames extracted by VGG-16, and inputs them into BiLSTM AutoEncoder for frame sequence anomaly detection and localization. The model is experimented on two different datasets with good results, validating the model generalization performance. Compared with the classical method, this model obtains higher precision(93.7%) of tamper points, which verifies the superiority of this deep learning model.
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Index Terms
- Video Inter-frame Tampering Detection Based on SN-VGG+BiLSTM-AE Composite Model
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Published In
December 2022
385 pages
ISBN:9781450397438
DOI:10.1145/3582197
Copyright © 2022 ACM.
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Published: 30 March 2023
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View all- Panchal HShah H(2023)Multiple forgery detection in digital video based on inconsistency in video quality assessment attributesMultimedia Systems10.1007/s00530-023-01123-929:4(2439-2454)Online publication date: 9-Jun-2023
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