Deepfake Video Detection Using Convolutional Vision Transformer at Mackenzie Wardle blog

Deepfake Video Detection Using Convolutional Vision Transformer. This work proposes a convolutional vision transformer for the detection of deepfakes and adds a cnn module to the vit architecture. In this work, we propose a generative convolutional vision transformer (genconvit) for deepfake video detection. The proposed genconvit model demonstrates robust performance in deepfake video detection, with an average accuracy of 95.8% and an auc value of 99.3% across the. A novel approach to detect deepfakes using a convolutional vision transformer (cvit) architecture that combines cnn and vit. Link to paper | link to ms thesis | link to ms thesis. A novel approach to detect deepfakes using a cnn and a vit architecture with extensive data preprocessing and. Wodajo and atnafu developed a deepfake detection framework employing a convolutional vision transformer (cvit). Deepfake video detection using convolutional vision transformer. In this work, we propose a convolutional vision transformer for the. Implementation code for our paper.

(PDF) Deepfake Video Detection Using Generative Convolutional Vision Transformer
from www.researchgate.net

Deepfake video detection using convolutional vision transformer. This work proposes a convolutional vision transformer for the detection of deepfakes and adds a cnn module to the vit architecture. Link to paper | link to ms thesis | link to ms thesis. Implementation code for our paper. In this work, we propose a convolutional vision transformer for the. Wodajo and atnafu developed a deepfake detection framework employing a convolutional vision transformer (cvit). A novel approach to detect deepfakes using a cnn and a vit architecture with extensive data preprocessing and. A novel approach to detect deepfakes using a convolutional vision transformer (cvit) architecture that combines cnn and vit. The proposed genconvit model demonstrates robust performance in deepfake video detection, with an average accuracy of 95.8% and an auc value of 99.3% across the. In this work, we propose a generative convolutional vision transformer (genconvit) for deepfake video detection.

(PDF) Deepfake Video Detection Using Generative Convolutional Vision Transformer

Deepfake Video Detection Using Convolutional Vision Transformer Implementation code for our paper. Wodajo and atnafu developed a deepfake detection framework employing a convolutional vision transformer (cvit). In this work, we propose a convolutional vision transformer for the. Deepfake video detection using convolutional vision transformer. The proposed genconvit model demonstrates robust performance in deepfake video detection, with an average accuracy of 95.8% and an auc value of 99.3% across the. A novel approach to detect deepfakes using a convolutional vision transformer (cvit) architecture that combines cnn and vit. Implementation code for our paper. A novel approach to detect deepfakes using a cnn and a vit architecture with extensive data preprocessing and. Link to paper | link to ms thesis | link to ms thesis. In this work, we propose a generative convolutional vision transformer (genconvit) for deepfake video detection. This work proposes a convolutional vision transformer for the detection of deepfakes and adds a cnn module to the vit architecture.

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