用深度学习检测假视频，识别细微篡改痕迹。
Unmasking Deep Fakes: Leveraging Deep Learning for Video Authenticity Detection
- 结合MTCNN人脸检测与EfficientNet-B5编码器进行特征提取
- 在DFDC数据集上达到86.82%的F1分数和93.80%的AUC
- 适合关注AI生成内容安全的研究者与从业者
深度伪造视频借助先进的生成技术，对数字媒体的真实性构成严峻挑战。随着深度伪造日益逼真，检测手段需具备识别细微不一致的能力。本文旨在利用深度学习方法检测深度伪造视频，采用卷积神经网络进行模式识别。我们使用MTCNN作为人脸检测器，EfficientNet-B5作为编码器模型，对视频是否为伪造进行分类。实验基于Kaggle的DFDC数据集，结果表明，该模型在测试集上获得42.78%的对数损失、93.80%的AUC值以及86.82%的F1分数，验证了其在深度伪造检测中的有效性。
原文摘要 · Abstract (English)
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.
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