粒度
计算机科学
面子(社会学概念)
人工智能
秩(图论)
卷积(计算机科学)
采样(信号处理)
机器学习
数据挖掘
模式识别(心理学)
数据科学
计算机视觉
数学
社会科学
滤波器(信号处理)
组合数学
社会学
人工神经网络
操作系统
作者
Han Chen,Yuezun Li,Dongdong Lin,Bin Li,Junqiang Wu
标识
DOI:10.1016/j.patcog.2022.109179
摘要
Recent years have witnessed significant advances in AI-based face manipulation techniques, known as DeepFakes, which has brought severe threats to society. Hence, an emerging and increasingly important research topic is how to detect DeepFake videos. In this paper, we propose a new DeepFake detection method based on Bi-granularity artifacts (BiG-Arts). We observe that the most of DeepFake video generation can commonly introduce bi-granularity artifacts: the intrinsic-granularity artifacts and extrinsic-granularity artifacts. Specifically, the intrinsic-granularity artifacts are caused by a common series of operations in model generation such as up-convolution or up-sampling, while the extrinsic-granularity artifacts are introduced by a common step in post-processing that blends the synthesized face to original video. To this end, we formulate DeepFake detection as multi-task learning problem, to simultaneously predict the intrinsic and extrinsic artifacts. Benefiting from the guidance of detecting Bi-granularity artifacts, our method is notably boosted in both within-datasets and cross-datasets scenarios. Extensive experiments are conducted on several DeepFake datasets, which corroborates the superiority of our method. Our method has been contributed as a part of the solution to achieve the Top-1 rank in DFGC competition (https://competitions.codalab.org/competitions/29583).
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