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.

翻译：深度伪造技术正模糊真实与虚幻的边界，可能引发恶意事件。通过利用新兴的深度伪造技术，研究者正颠覆性地创作深度伪造艺术品（deeparts），进一步拉近现实与幻想的距离。为应对潜在伦理问题，本文构建了深度伪技检测数据库（DDDB），包含一组高质量传统艺术图像（conarts）和五组由五种最先进深度伪造模型生成的深度艺术图像。该数据库使我们能够探索一次性深度伪技检测与持续深度伪技检测。针对这两个新问题，我们在所构建的DDDB上提出了四种基准评估方案和四类解决方案。综合研究表明，所提方案在基准数据集上具有有效性，为深度伪技检测领域开辟了更值得探索的方向。所构建的基准数据集和源代码将公开提供。