A novel ResNet-Swish-Dense54 model is presented that improves the deepfakes detection accuracy by introducing a very little computational burden. Reliable and accurate identification of deepfakes due to the capability of the proposed approach to tackle the model over-fitting.
Dec 2, 2022
In this work, we have presented deep learning (DL)-based approach namely the convolutional long short-term memory (C-LSTM) method for deepfakes detection from ...
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The presented approach has used deep learning (DL)-based approach namely ResNet-Swish-Dense54 for reliable and accurate detection of deepfakes, and ...
Oct 15, 2024 · In this study we explore automatic key detection and generation methods, frameworks, algorithms, and tools for identifying deepfakes (audio, images, and videos)
A systematic review of deepfake detection and generation ...
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The presented approach has used deep learning (DL)-based approach namely ResNet-Swish-Dense54 for reliable and accurate detection of deepfakes, and ...
Oct 17, 2024 · Methods such as ResNet-Swish-Dense54, which depend on facial extraction, may need to be more reliable due to facial appearance and video quality ...
The evaluation involves testing against adversarial attacks, and the explainability power of the ResNet-Swish-. Dense54 model is demonstrated through heatmap ...
Nawaz et al. [Citation13] present a deep learning approach for deepfake detection in their paper “ResNet-Swish-Dense54: a deep learning approach ...
Methods such as ResNet-Swish-Dense54, which depend on facial extraction, may need to be more reliable due to facial appearance and video quality discrepancies.
Nawaz M, Javed A, Irtaza A (2022) ResNet-Swish-Dense54: a deep learning approach for deepfakes detection. Visual Comput 1–22 https://doi.org/10.1007/s00371 ...