论文标题
当手工制作的功能和深度功能符合不匹配的培训和测试集以进行深泡沫检测
When Handcrafted Features and Deep Features Meet Mismatched Training and Test Sets for Deepfake Detection
论文作者
论文摘要
现在，合成视觉媒体产生和操纵的加速增长已经达到了引起重大关注并对社会造成巨大恐吓的地步。当务之急需要自动检测网络涉及虚假数字内容，并避免危险人造信息的传播以应对这种威胁。在本文中，我们利用和比较了两种手工制作的功能（Sift和Hog）以及两种深层特征（Xpection和CNN+RNN），以进行深层检测任务。当训练集和测试集之间存在不匹配时，我们还会检查这些功能的性能。评估是在著名的FaceForensics ++数据集上进行的，该数据集包含四个子数据集，深击，face2face，faceswap和neuralTextures。最好的结果来自Xception，当训练和测试集均来自同一子数据库时，精度可能会超过99 \％。相比之下，当训练集不匹配测试集时，结果急剧下降。这种现象揭示了创建通用深击检测系统的挑战。
The accelerated growth in synthetic visual media generation and manipulation has now reached the point of raising significant concerns and posing enormous intimidations towards society. There is an imperative need for automatic detection networks towards false digital content and avoid the spread of dangerous artificial information to contend with this threat. In this paper, we utilize and compare two kinds of handcrafted features(SIFT and HoG) and two kinds of deep features(Xception and CNN+RNN) for the deepfake detection task. We also check the performance of these features when there are mismatches between training sets and test sets. Evaluation is performed on the famous FaceForensics++ dataset, which contains four sub-datasets, Deepfakes, Face2Face, FaceSwap and NeuralTextures. The best results are from Xception, where the accuracy could surpass over 99\% when the training and test set are both from the same sub-dataset. In comparison, the results drop dramatically when the training set mismatches the test set. This phenomenon reveals the challenge of creating a universal deepfake detection system.
