论文标题
针对操纵的面部检测的细心语义探索
Attentive Semantic Exploring for Manipulated Face Detection
论文作者
论文摘要
近年来，面部操纵方法迅速发展，其对社会的潜在风险解释了对检测方法的研究的出现。但是，由于操纵方法的多样性和伪造图像的高质量，检测方法缺乏概括能力。为了解决问题，我们发现将图像分割为语义片段可能是有效的，因为歧视性缺陷和扭曲与此类片段密切相关。此外，要突出片段中的歧视区域并衡量对每个片段的最终预测的贡献，可以有效地提高概括能力。因此，我们提出了一种基于多级面部语义分割和级联注意机制的新型操纵面检测方法。为了评估我们的方法，我们重建了两个数据集：GGFI和FFMI，还收集了两个开源数据集。四个数据集的实验验证了我们对其他最先进的方法的优势，尤其是其概括能力。
Face manipulation methods develop rapidly in recent years, whose potential risk to society accounts for the emerging of researches on detection methods. However, due to the diversity of manipulation methods and the high quality of fake images, detection methods suffer from a lack of generalization ability. To solve the problem, we find that segmenting images into semantic fragments could be effective, as discriminative defects and distortions are closely related to such fragments. Besides, to highlight discriminative regions in fragments and to measure contribution to the final prediction of each fragment is efficient for the improvement of generalization ability. Therefore, we propose a novel manipulated face detection method based on Multilevel Facial Semantic Segmentation and Cascade Attention Mechanism. To evaluate our method, we reconstruct two datasets: GGFI and FFMI, and also collect two open-source datasets. Experiments on four datasets verify the advantages of our approach against other state-of-the-arts, especially its generalization ability.
