  Deepfake technologies have been blurring the boundaries between the real and
unreal, likely resulting in malicious events. By leveraging newly emerged
deepfake technologies, deepfake researchers have been making a great upending
to create deepfake artworks (deeparts), which are further closing the gap
between reality and fantasy. To address potentially appeared ethics questions,
this paper establishes a deepart detection database (DDDB) that consists of a
set of high-quality conventional art images (conarts) and five sets of deepart
images generated by five state-of-the-art deepfake models. This database
enables us to explore once-for-all deepart detection and continual deepart
detection. For the two new problems, we suggest four benchmark evaluations and
four families of solutions on the constructed DDDB. The comprehensive study
demonstrates the effectiveness of the proposed solutions on the established
benchmark dataset, which is capable of paving a way to more interesting
directions of deepart detection. The constructed benchmark dataset and the
source code will be made publicly available.

Computer Vision and Pattern Recognition;Machine Learning