用卷积神经网络检测人脸伪造图像，准确率高达99%
Deepfake Detection of Face Images based on a Convolutional Neural Network
- 基于ResNet-50迁移学习，微调后添加单输出神经元判断真伪
- 在多样伪造数据集上达到F1分数0.97、AUC 0.99的优异表现
- 适合关注深度伪造识别与信息安全的研究者和开发者
假新闻，尤其是由机器学习生成的深度伪造（非真实图像或视频内容），近年来已成为严重问题。随着算法的发展，普通人也能轻松生成此类虚假内容，尤其对政治人物和公众人物构成重大威胁。本文通过构建基于卷积神经网络的模型，检测包含人类肖像的伪造图像。采用预训练的ResNet-50模型，通过添加全连接输出层（含一个神经元）实现图像真伪分类，并结合微调与迁移学习优化参数。训练数据使用“Diverse Face Fake Dataset”——一个涵盖多种图像篡改方法且面孔多样性高的数据集。最终模型在测试中达到精确率0.98、召回率0.96、F1分数0.97，曲线下面积（AUC）为0.99。
原文摘要 · Abstract (English)
Fake News and especially deepfakes (generated, non-real image or video content) have become a serious topic over the last years. With the emergence of machine learning algorithms it is now easier than ever before to generate such fake content, even for private persons. This issue of generated fake images is especially critical in the context of politics and public figures. We want to address this conflict by building a model based on a Convolutions Neural Network in order to detect such generated and fake images showing human portraits. As a basis, we use a pre-trained ResNet-50 model due to its effectiveness in terms of classifying images. We then adopted the base model to our task of classifying a single image as authentic/real or fake by adding an fully connected output layer containing a single neuron indicating the authenticity of an image. We applied fine tuning and transfer learning to develop the model and improve its parameters. For the training process we collected the image data set "Diverse Face Fake Dataset" containing a wide range of different image manipulation methods and also diversity in terms of faces visible on the images. With our final model we reached the following outstanding performance metrics: precision = 0.98, recall 0.96, F1-Score = 0.97 and an area-under-curve = 0.99.
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