deepfake视频检测-基于卷积Vision-Transformer实现的deepfake视频检测源码+模型+运行说明.zip

# CViT ### Deepfake Video Detection Using Convolutional Vision Transformer Implementation code for our paper. [link to paper](https://arxiv.org/abs/2102.11126) | [link to MS Thesis](http://etd.aau.edu.et/handle/123456789/24209) | [link to MS Thesis defense PPT file](https://github.com/erprogs/CViT/blob/main/CViT.pptx) ### Requirements: * Pytorch >=1.4 ### DL library used for face extraction * helpers_read_video_1.py * helpers_face_extract_1.py * blazeface.py * blazeface.pth ### Preprocessing extractfaces.py<br /> &nbsp;&nbsp;&nbsp;Face extraction from video. <br /> &nbsp;&nbsp;&nbsp;The code works for DFDC dataset. You can test it using the sample data provided. ### Weights deepfake_cvit_gpu_ep_50.pth - Full model weight. <br /> deepfake_cvit_gpu_inference_ep_50.pth - For detection. <br /> ### Predict CViT python cvit_prediction.py <br /> &nbsp;&nbsp;&nbsp; Predicts whether a video is Deepfake or not.<br /> &nbsp;&nbsp;&nbsp; Prediction value <0.5 - REAL <br /> &nbsp;&nbsp;&nbsp; Prediction value >=5 - FAKE ### Train CViT To train the model on your own you can use the following parameters:<br /> &nbsp;&nbsp;e: epoch <br/> &nbsp;&nbsp;s: session - **(g)** - GPU or **(t)** - TPU <br/> &nbsp;&nbsp;w: weight decay default= 0.0000001 <br/> &nbsp;&nbsp;l: learning rate default=0.001 <br/> &nbsp;&nbsp;d: path file <br/> &nbsp;&nbsp;b: batch size, defualt=32 <br/> python cvit_train.py -e 10 -s 'g' -l 0.0001 -w 0.0000001 -d sample_train_data/ ### Authors **Deressa Wodajo** <br /> **Solomon Atnafu (PhD)** ## Bibtex ```bash @misc{wodajo2021deepfake, title={Deepfake Video Detection Using Convolutional Vision Transformer}, author={Deressa Wodajo and Solomon Atnafu}, year={2021}, eprint={2102.11126}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```