Deepfakes have raised significant concerns due to their potential to spread false information and compromise digital media integrity. In this work, we propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake video detection. Our model combines ConvNeXt and Swin Transformer models for feature extraction, and it utilizes Autoencoder and Variational Autoencoder to learn from the latent data distribution. By learning from the visual artifacts and latent data distribution, GenConViT achieves improved performance in detecting a wide range of deepfake videos. The model is trained and evaluated on DFDC, FF++, DeepfakeTIMIT, and Celeb-DF v2 datasets, achieving high classification accuracy, F1 scores, and AUC values. 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 tested datasets. Our proposed model addresses the challenge of generalizability in deepfake detection by leveraging visual and latent features and providing an effective solution for identifying a wide range of fake videos while preserving media integrity. The code for GenConViT is available at https://github.com/erprogs/GenConViT.

翻译：深度伪造技术因其可能传播虚假信息并破坏数字媒体完整性而引发了重大关注。本文提出了一种生成式卷积视觉Transformer（GenConViT）用于深度伪造视频检测。该模型结合了ConvNeXt和Swin Transformer模型进行特征提取，并利用自编码器和变分自编码器从潜在数据分布中学习。通过从视觉伪影和潜在数据分布中学习，GenConViT在检测多种深度伪造视频方面取得了更优性能。该模型在DFDC、FF++、DeepfakeTIMIT和Celeb-DF v2数据集上进行了训练与评估，实现了高分类准确率、F1分数和AUC值。所提出的GenConViT模型在深度伪造视频检测中展现出稳健性能，在测试数据集上的平均准确率达95.8%，AUC值达99.3%。该模型通过利用视觉特征和潜在特征，解决了深度伪造检测中的泛化性挑战，为识别多种伪造视频提供了有效解决方案，同时维护了媒体完整性。GenConViT的代码已开源：https://github.com/erprogs/GenConViT。