  The detection of digital face manipulation in video has attracted extensive
attention due to the increased risk to public trust. To counteract the
malicious usage of such techniques, deep learning-based deepfake detection
methods have been developed and have shown impressive results. However, the
performance of these detectors is often evaluated using benchmarks that hardly
reflect real-world situations. For example, the impact of various video
processing operations on detection accuracy has not been systematically
assessed. To address this gap, this paper first analyzes numerous real-world
influencing factors and typical video processing operations. Then, a more
systematic assessment methodology is proposed, which allows for a quantitative
evaluation of a detector's robustness under the influence of different
processing operations. Moreover, substantial experiments have been carried out
on three popular deepfake detectors, which give detailed analyses on the impact
of each operation and bring insights to foster future research.
