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Multimodal Cognitive Learning for Media Forgery Detection: ..:

, In: 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA),

Abirami, A. ; Bhuvaneswari, S. ; Krithika, K.. - p. 1083-1091 , 2023

Link: https://doi.org/10.1109/ICIMIA60377.2023.10426415

RT 
T1 <cTitle>2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)</cTitle>: T1 <Title>Multimodal Cognitive Learning for Media Forgery Detection: A Comprehensive Framework Combining Random Forest and Deep Ensemble Architectures (Xception, ResNeXt) across Image, Video, and Audio Modalities</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10426415&Exemplar=1&LAN=DE
				
A1 Abirami, A.
A1 Bhuvaneswari, S.
A1 Krithika, K
A1 Nithyasree, I
A1 Prashithaa Abhirami, B
YR 2023
K1 Deepfakes
K1 Visualization
K1 Audio recording
K1 Spatiotemporal phenomena
K1 Convolutional neural networks
K1 Security
K1 Spectrogram
K1 Deepfake detection
K1 Multi-modal system
K1 Image manipulation
K1 Video forgery
K1 Audio spoofing
K1 Convolutional Neural Networks (CNNs)
K1 Xception
K1 ResNeXT
K1 Spatiotemporal analysis
K1 Mel spectrograms
K1 F1 score
K1 ROC curve
K1 AUC
SP 1083
OP 1091
LK http://dx.doi.org/https://doi.org/10.1109/ICIMIA60377.2023.10426415
DO https://doi.org/10.1109/ICIMIA60377.2023.10426415
SF ELIB - SuUB Bremen

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