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Video Deepfake classification using particle swarm optimization-based evolving ensemble models

delete2024-04-01
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AI
L
Li Zhang *
D
Dezong Zhao
C
Chee Peng Lim
H
Houshyar Asadi
黄浩乾 (Haoqian Huang)
Y
Yonghong Yu
高榕 (Rong Gao)
DOI:10.1016/j.knosys.2024.111461delete
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Abstract

Abstract

En 中文
The recent breakthrough of deep learning based generative models has led to the escalated generation of photorealistic synthetic videos with significant visual quality. Automated reliable detection of such forged videos requires the extraction of fine-grained discriminative spatial-temporal cues. To tackle such challenges, we propose weighted and evolving ensemble models comprising 3D Convolutional Neural Networks (CNNs) and CNNRecurrent Neural Networks (RNNs) with Particle Swarm Optimization (PSO) based network topology and hyperparameter optimization for video authenticity classification. A new PSO algorithm is proposed, which embeds Muller's method and fixed-point iteration based leader enhancement, reinforcement learning-based optimal search action selection, a petal spiral simulated search mechanism, and cross-breed elite signal generation based on adaptive geometric surfaces. The PSO variant optimizes the RNN topologies in CNN-RNN, as well as key learning configurations of 3D CNNs, with the attempt to extract effective discriminative spatial-temporal cues. Both weighted and evolving ensemble strategies are used for ensemble formulation with aforementioned optimized networks as base classifiers. In particular, the proposed PSO algorithm is used to identify optimal subsets of optimized base networks for dynamic ensemble generation to balance between ensemble complexity and performance. Evaluated using several well-known synthetic video datasets, our approach outperforms existing studies and various ensemble models devised by other search methods with statistical significance for video authenticity classification. The proposed PSO model also illustrates statistical superiority over a number of search methods for solving optimization problems pertaining to a variety of artificial landscapes with diverse geometrical layouts.
Keywords:
Video deepfake classification
Hybrid deep neural network
3d convolutional neural network
Evolutionary algorithm
Evolving ensemble classifier
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K
Knowledge-Based Systems
IF:
7.6
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Citations:
4.5W

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H
Hohai University
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Papers: 1.8W
Citations: 2.1W
R
Royal Holloway University London
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Papers: 2.2K
Citations: 47
U
university of london
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Papers: 19.7W
Citations: 305
U
university of glasgow
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Citations: 37
D
Deakin University
Scholars:
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Papers: 2.1W
Citations: 2.8W
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