Zhang Hangchuan, Hu Caiping, Min Shiyu, Sui Hui, Zhou Guola
Department of Computer Engineering, Jinling Institute of Technology, Nanjing, Jiangsu, China.
PLoS One. 2024 Dec 13;19(12):e0311366. doi: 10.1371/journal.pone.0311366. eCollection 2024.
With the advancement of deep forgery techniques, particularly propelled by generative adversarial networks (GANs), identifying deepfake faces has become increasingly challenging. Although existing forgery detection methods can identify tampering details within manipulated images, their effectiveness significantly diminishes in complex scenes, especially in low-quality images subjected to compression. To address this issue, we proposed a novel deep face forgery video detection model named Two-Stream Feature Domain Fusion Network (TSFF-Net). This model comprises spatial and frequency domain feature extraction branches, a feature extraction layer, and a Transformer layer. In the feature extraction module, we utilize the Scharr operator to extract edge features from facial images, while also integrating frequency domain information from these images. This combination enhances the model's ability to detect low-quality deepfake videos. Experimental results demonstrate the superiority of our method, achieving detection accuracies of 97.7%, 91.0%, 98.9%, and 90.0% on the FaceForensics++ dataset for Deepfake, Face2Face, FaceSwap, and NeuralTextures forgeries, respectively. Additionally, our model exhibits promising results in cross-dataset experiments.. The code used in this study is available at: https://github.com/hwZHc/TSFF-Net.git.
随着深度伪造技术的发展，尤其是在生成对抗网络（GANs）的推动下，识别深度伪造面部变得越来越具有挑战性。尽管现有的伪造检测方法可以识别被操纵图像中的篡改细节，但在复杂场景中，尤其是在经过压缩的低质量图像中，它们的有效性会显著降低。为了解决这个问题，我们提出了一种名为双流特征域融合网络（TSFF-Net）的新型深度面部伪造视频检测模型。该模型由空间和频域特征提取分支、一个特征提取层和一个Transformer层组成。在特征提取模块中，我们利用Scharr算子从面部图像中提取边缘特征，同时还整合这些图像的频域信息。这种结合增强了模型检测低质量深度伪造视频的能力。实验结果证明了我们方法的优越性，在FaceForensics++数据集上，对于Deepfake、Face2Face、FaceSwap和NeuralTextures伪造分别达到了97.7%、91.0%、98.9%和90.0%的检测准确率。此外，我们的模型在跨数据集实验中也表现出了有前景的结果。本研究中使用的代码可在以下网址获取：https://github.com/hwZHc/TSFF-Net.git。
