揭穿深度伪造：利用深度学习进行视频真实性检测
Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection
AI总结：
针对Deepfake视频对数字媒体真实性的威胁，本文提出结合MTCNN人脸检测器与EfficientNet-B5编码器的深度学习检测方法，在Kaggle DFDC数据集上取得了93.80%的AUC与86.82%的F1值。
摘要：
如今通过先进人工智能方法生成的Deepfake（深度伪造）视频，对数字媒体的真实性提出了新挑战。随着Deepfake的逼真度日益提升，检测这类视频需要能够识别细微不一致性的先进方法。本文的核心动机是利用深度学习技术，具体来说是卷积神经网络，来识别深度伪造视频。深度学习在模式识别方面表现出色，因此是检测Deepfake中复杂篡改的理想方法。本文中，我们采用MTCNN作为人脸检测器，EfficientNet-B5作为编码器模型，来预测视频是否为深度伪造。我们使用Kaggle DFDC数据集进行训练和评估。结果显示，我们的深度伪造检测模型在Kaggle的DFDC数据集上取得了42.78%的对数损失、93.80%的AUC以及86.82%的F1值。
英文摘要：
Deepfake videos, produced through advanced artificial intelligence methods now a days, pose a new challenge to the truthfulness of the digital media. As Deepfake becomes more convincing day by day, detecting them requires advanced methods capable of identifying subtle inconsistencies. The primary motivation of this paper is to recognize deepfake videos using deep learning techniques, specifically by using convolutional neural networks. Deep learning excels in pattern recognition, hence, makes it an ideal approach for detecting the intricate manipulations in deepfakes. In this paper, we consider using MTCNN as a face detector and EfficientNet-B5 as encoder model to predict if a video is deepfake or not. We utilize training and evaluation dataset from Kaggle DFDC. The results shows that our deepfake detection model acquired 42.78% log loss, 93.80% AUC and 86.82% F1 score on kaggle's DFDC dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
