TL;DR
A novel method for video deepfake detection that assimilates the Pelican Optimization algorithm with a DL model jointly named as Pelican Attention Stacked Bidirectional Long-Short Term Memory (PAttSBiL), aimed at improving recognition accuracy and efficacy is presented.
This study uses ResNet-50 architecture, a well-known Convolutional Neural Network model, to identify tampered videos using DL, and indicates that the proposed model works effectively compared to existing deepfake methods.
A novel multimodal approach to deepfake detection, integrating spatial, temporal, and audio features, which is highly efficient in addressing deepfake challenges in the digital forensic field and tested on various benchmarking datasets, yielding higher accuracy than current methods.
The approach begins with detecting the faces and then applying face alignment, crop, resizing, and normalization to guarantee consistency, and has proved to be quite efficient in deepfake detection, as the accuracy of the GRU model was the best, at 98%.
Deepfake videos generated using modern deep learning techniques pose significant threats to digital media authenticity and public trust. These manipulated videos often appear highly realistic, making manual verification difficult. This paper proposes an efficient spatiotemporal deepfake detection framework that combine...
The rapid growth of deep learning technologies and social media has led to a massive amount of deepfake content. The deepfake media is often utilized for spreading misinformation, identity theft, falsification of data, spoofing, and hate speech. Studies indicate that over 96% of deepfake videos online are used for mali...
The development of deepfakes is becoming a serious threat to multimedia security, therefore making the need for robust and efficient detection systems vital. In this paper, an in-depth review of deep learning approaches that have been developed towards the automatic detection of deepfake videos is carried out. This inc...
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