论文标题
用自融合的图像检测深击
Detecting Deepfakes with Self-Blended Images
论文作者
论文摘要
在本文中，我们介绍了称为自融合图像（SBI）的新型合成训练数据，以检测深击。 SBI是通过将伪源和来自单个原始图像的目标图像混合而产生的，从而再现了共同的伪造工件（例如，源和目标图像之间的混合边界和统计不一致）。 SBI背后的关键思想是，更通用和几乎无法识别的假样品鼓励分类器学习通用和稳健的表示，而不会过于适应特定于操纵的文物。我们通过遵循标准的交叉数据库和交叉处理协议，将我们的方法与FF ++，CDF，DFD，DFD，DFDC，DFDC，DFDC，DFDC和FFIW数据集进行比较。广泛的实验表明，我们的方法改善了对未知操作和场景的模型概括。特别是，在DFDC和DFDCP上，现有方法在培训和测试集之间遇到域间隙，我们的方法在跨数据库评估中分别优于基线4.90％和11.78％。
In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source and target images from single pristine images, reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images). The key idea behind SBIs is that more general and hardly recognizable fake samples encourage classifiers to learn generic and robust representations without overfitting to manipulation-specific artifacts. We compare our approach with state-of-the-art methods on FF++, CDF, DFD, DFDC, DFDCP, and FFIW datasets by following the standard cross-dataset and cross-manipulation protocols. Extensive experiments show that our method improves the model generalization to unknown manipulations and scenes. In particular, on DFDC and DFDCP where existing methods suffer from the domain gap between the training and test sets, our approach outperforms the baseline by 4.90% and 11.78% points in the cross-dataset evaluation, respectively.
