Numerous well-performing facial expression recognition algorithms suffer from severe slippage when trained on one dataset and tested on another, due to inconsistencies in facial expression datasets caused by different...
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Recent advances in deep learning have empowered media synthesis and alteration to achieve levels of realism that were previously unheard of. Artificial intelligence is a potent tool that may be used to modify digital ...
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ISBN:
(纸本)9783031490989;9783031490996
Recent advances in deep learning have empowered media synthesis and alteration to achieve levels of realism that were previously unheard of. Artificial intelligence is a potent tool that may be used to modify digital data, such as images, videos, and audio files, through the use of emerging deepfake technologies. Deepfake technology has the potential to significantly affect the reliability of multimedia data through the synthesis of fake media. Significant ramifications arise from this for individuals, organizations, and society at large. With the pace and accessibility of social media, convincing deepfakes can swiftly reach millions of people and adversely influence public opinion. To this end, we propose a multi-modal feature-based classification model that can distinguish between deepfake and real videos efficiently. We have used prefabricated image features as well as a variety of Convolutional Neural Network (CNN) model-generated features, including ResNet50, ResNet101, VGG16, and VGG19. The fake videos are taken up for further investigation to detect their source of origin. We propose a CNN-based classifier for deepfake detection and also explore the efficiency of multiple feature-based classifiers in this respect. This enables us to evaluate the comparative performance of both. The proposed model achieves an accuracy of 99.06% on deepfake classification and 98.75% on source identification when tested on a publicly available FaceForensics++ dataset.
Alzheimer’s disease (AD) is a common chronic neurodegenerative disease and the accurate prediction of the clinical cognitive performance is important for diagnosis and treatment. Recently, multi-task feature learning...
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The paper concerns the methodology for speculative support for query execution in Relational Database Management Systems (RDBMSs). It discusses and develops our proposal of supporting the RDBMS query execution based o...
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作者：
Yoshida, MakiMouri, Koichi
Network Security Research Institute 4-2-1 Nukui-Kitamachi Koganei Tokyo 184-8795 Japan Ritsumeikan University
College of Information Science and Engineering 1-1-1 Nojihigashi Kusatsu Shiga 525-8577 Japan
A large number of deep learning models have been applied in a wide range of fields nowadays. However, most existing models can only generalize to the categories in the training set and are unable to learn new categori...
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Autonomous vehicles, lifestyles, and businesses related to the tourism industry are facing a lot of security issues when the digital revolution is considered in the digital version of security management. The aim of t...
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With the advent of a paradigm shift in the area of data communication, Internet of things (IoT) has remarkably transformed the whole facets of information sharing and data aggregation. It comprises of numerous heterog...
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