Understanding and Identifying Artwork Plagiarism with the Wisdom of Designers

The wide sharing and rapid dissemination of digital artworks has aggravated the issues of plagiarism, raising significant concerns in cultural preservation and copyright protection. Yet, modes of plagiarism are formally uncharted, causing rough plagiarism detection practices with duplicate checking. This work is thus devoted to understanding artwork plagiarism, with poster design as the running case, for building more dedicated detection techniques. As the first study of such, we elaborate on 8 elements that form unique posters and 6 judgement criteria for plagiarism using an exploratory study with designers. Second, we build a novel poster dataset with plagiarism annotations according to the criteria. Third, we propose models, leveraging the combination of primary elements and criteria of plagiarism, to find suspect instances in a retrieval process. The models are trained under the context of modern artwork and evaluated on the poster plagiarism dataset. The proposal is shown to outperform the baseline with superior Top-K accuracy (~33%) and retrieval performance (~42%).