Semantic to Structure: Learning Structural Representations for Infringement Detection
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912228817502208 |
|---|---|
| author | Huang, Chuanwei Jia, Zexi Fei, Hongyan Zhu, Yeshuang Yuan, Zhiqiang Zhang, Jinchao Zhou, Jie |
| author_facet | Huang, Chuanwei Jia, Zexi Fei, Hongyan Zhu, Yeshuang Yuan, Zhiqiang Zhang, Jinchao Zhou, Jie |
| contents | Structural information in images is crucial for aesthetic assessment, and it is widely recognized in the artistic field that imitating the structure of other works significantly infringes on creators' rights. The advancement of diffusion models has led to AI-generated content imitating artists' structural creations, yet effective detection methods are still lacking. In this paper, we define this phenomenon as "structural infringement" and propose a corresponding detection method. Additionally, we develop quantitative metrics and create manually annotated datasets for evaluation: the SIA dataset of synthesized data, and the SIR dataset of real data. Due to the current lack of datasets for structural infringement detection, we propose a new data synthesis strategy based on diffusion models and LLM, successfully training a structural infringement detection model. Experimental results show that our method can successfully detect structural infringements and achieve notable improvements on annotated test sets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07323 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic to Structure: Learning Structural Representations for Infringement Detection Huang, Chuanwei Jia, Zexi Fei, Hongyan Zhu, Yeshuang Yuan, Zhiqiang Zhang, Jinchao Zhou, Jie Computer Vision and Pattern Recognition Structural information in images is crucial for aesthetic assessment, and it is widely recognized in the artistic field that imitating the structure of other works significantly infringes on creators' rights. The advancement of diffusion models has led to AI-generated content imitating artists' structural creations, yet effective detection methods are still lacking. In this paper, we define this phenomenon as "structural infringement" and propose a corresponding detection method. Additionally, we develop quantitative metrics and create manually annotated datasets for evaluation: the SIA dataset of synthesized data, and the SIR dataset of real data. Due to the current lack of datasets for structural infringement detection, we propose a new data synthesis strategy based on diffusion models and LLM, successfully training a structural infringement detection model. Experimental results show that our method can successfully detect structural infringements and achieve notable improvements on annotated test sets. |
| title | Semantic to Structure: Learning Structural Representations for Infringement Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.07323 |