  There have been emerging a number of benchmarks and techniques for the
detection of deepfakes. However, very few works study the detection of
incrementally appearing deepfakes in the real-world scenarios. To simulate the
wild scenes, this paper suggests a continual deepfake detection benchmark
(CDDB) over a new collection of deepfakes from both known and unknown
generative models. The suggested CDDB designs multiple evaluations on the
detection over easy, hard, and long sequence of deepfake tasks, with a set of
appropriate measures. In addition, we exploit multiple approaches to adapt
multiclass incremental learning methods, commonly used in the continual visual
recognition, to the continual deepfake detection problem. We evaluate existing
methods, including their adapted ones, on the proposed CDDB. Within the
proposed benchmark, we explore some commonly known essentials of standard
continual learning. Our study provides new insights on these essentials in the
context of continual deepfake detection. The suggested CDDB is clearly more
challenging than the existing benchmarks, which thus offers a suitable
evaluation avenue to the future research. Both data and code are available at
https://github.com/Coral79/CDDB.
