The proliferation of deepfake media is raising concerns among the public and relevant authorities. It has become essential to develop countermeasures against forged faces in social media. This paper presents a comprehensive study on two new countermeasure tasks: multi-face forgery detection and segmentation in-the-wild. Localizing forged faces among multiple human faces in unrestricted natural scenes is far more challenging than the traditional deepfake recognition task. To promote these new tasks, we have created the first large-scale dataset posing a high level of challenges that is designed with face-wise rich annotations explicitly for face forgery detection and segmentation, namely OpenForensics. With its rich annotations, our OpenForensics dataset has great potentials for research in both deepfake prevention and general human face detection. We have also developed a suite of benchmarks for these tasks by conducting an extensive evaluation of state-of-the-art instance detection and segmentation methods on our newly constructed dataset in various scenarios. The dataset, benchmark results, codes, and supplementary materials will be publicly available on our project page: https://sites.google.com/view/ltnghia/research/openforensics

中文翻译：

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OpenForensics：用于野外多面伪造检测和分割的大规模具有挑战性的数据集

Deepfake 媒体的泛滥引起了公众和相关当局的关注。制定针对社交媒体中伪造面孔的对策已变得至关重要。本文对两个新的对抗任务进行了全面的研究：多面伪造检测和野外分割。在不受限制的自然场景中定位多个人脸中的伪造人脸比传统的深度伪造识别任务更具挑战性。为了促进这些新任务，我们创建了第一个具有高水平挑战的大规模数据集，该数据集设计有专门用于面部伪造检测和分割的面部丰富的注释，即 OpenForensics。凭借其丰富的注释，我们的 OpenForensics 数据集在深度伪造预防和一般人脸检测方面具有巨大的研究潜力。我们还通过对各种场景中新构建的数据集的最新实例检测和分割方法进行广泛评估，为这些任务开发了一套基准测试。数据集、基准测试结果、代码和补充材料将在我们的项目页面上公开提供：https://sites.google.com/view/ltnghia/research/openforensics