The growing diversity of digital face manipulation techniques has led to an urgent need for a universal and robust detection technology to mitigate the risks posed by malicious forgeries. We present a blended-based detection approach that has robust applicability to unseen datasets. It combines a method for generating synthetic training samples, i.e., reconstructed blended images, that incorporate potential deepfake generator artifacts and a detection model, a multi-scale feature reconstruction network, for capturing the generic boundary artifacts and noise distribution anomalies brought about by digital face manipulations. Experiments demonstrated that this approach results in better performance in both cross-manipulation detection and cross-dataset detection on unseen data.

翻译：数字人脸操控技术的多样性日益增长，亟需一种通用且鲁棒的检测技术来应对恶意伪造带来的风险。我们提出一种基于混合的检测方法，该方法对未见数据集具有鲁棒适用性。它结合了一种合成训练样本生成方法（即重建混合图像），该方法融合了潜在的深度伪造生成器伪影，以及一个检测模型——多尺度特征重建网络，用于捕捉数字人脸操控带来的通用边界伪影和噪声分布异常。实验表明，该方法在未知数据上的跨操控检测和跨数据集检测中均展现出更优性能。