PDF(Pubmed)
Abstract：
Images and videos containing fake faces are the most common type of digital manipulation. Such content can lead to negative consequences by spreading false information. The use of machine learning algorithms to produce fake face images has made it challenging to distinguish between genuine and fake content. Face manipulations are categorized into four basic groups: entire face synthesis, face identity manipulation (deepfake), facial attribute manipulation and facial expression manipulation. The study utilized lightweight convolutional neural networks to detect fake face images generated by using entire face synthesis and generative adversarial networks. The dataset used in the training process includes 70,000 real images in the FFHQ dataset and 70,000 fake images produced with StyleGAN2 using the FFHQ dataset. 80% of the dataset was used for training and 20% for testing. Initially, the MobileNet, MobileNetV2, EfficientNetB0, and NASNetMobile convolutional neural networks were trained separately for the training process. In the training, the models were pre-trained on ImageNet and reused with transfer learning. As a result of the first trainings EfficientNetB0 algorithm reached the highest accuracy of 93.64%. The EfficientNetB0 algorithm was revised to increase its accuracy rate by adding two dense layers (256 neurons) with ReLU activation, two dropout layers, one flattening layer, one dense layer (128 neurons) with ReLU activation function, and a softmax activation function used for the classification dense layer with two nodes. As a result of this process accuracy rate of 95.48% was achieved with EfficientNetB0 algorithm. Finally, the model that achieved 95.48% accuracy was used to train MobileNet and MobileNetV2 models together using the stacking ensemble learning method, resulting in the highest accuracy rate of 96.44%.
摘要：
包含假脸的图像和视频是最常见的数字操纵类型。此类内容可能会通过传播虚假信息而导致负面后果。使用机器学习算法来生成假人脸图像使得区分真假内容变得很有挑战性。面部操作分为四个基本组：整个面部合成，面部身份操纵(deepfake)，面部属性操纵和面部表情操纵。该研究利用轻量级卷积神经网络来检测使用整个人脸合成和生成对抗网络生成的假人脸图像。训练过程中使用的数据集包括FFHQ数据集中的70,000个真实图像和使用FFHQ数据集用StyleGAN2产生的70,000个假图像。80%的数据集用于训练，20%用于测试。最初，MobileNet,MobileNetV2、EfficientNetB0和NASNetMobile卷积神经网络被分别训练用于训练过程。在训练中,模型在ImageNet上进行了预训练,并与迁移学习一起重用.作为第一次训练的结果，EfficientNetB0算法达到了93.64%的最高精度。修改了EfficientNetB0算法，通过添加两个具有ReLU激活的密集层（256个神经元）来提高其准确率，两个dropout层，一个平坦层，一个具有ReLU激活功能的致密层（128个神经元），和用于具有两个节点的分类密集层的softmax激活函数。结果，EfficientNetB0算法实现了95.48％的过程准确率。最后,使用堆叠集成学习方法，将达到95.48%精度的模型用于一起训练MobileNet和MobileNetV2模型，的最高准确率为96.44%。
