State-of-the-Art in Nudity Classification: A Comparative Analysis

Autor:	Akyon, Fatih Cagatay, Temizel, Alptekin
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	This paper presents a comparative analysis of existing nudity classification techniques for classifying images based on the presence of nudity, with a focus on their application in content moderation. The evaluation focuses on CNN-based models, vision transformer, and popular open-source safety checkers from Stable Diffusion and Large-scale Artificial Intelligence Open Network (LAION). The study identifies the limitations of current evaluation datasets and highlights the need for more diverse and challenging datasets. The paper discusses the potential implications of these findings for developing more accurate and effective image classification systems on online platforms. Overall, the study emphasizes the importance of continually improving image classification models to ensure the safety and well-being of platform users. The project page, including the demonstrations and results is publicly available at https://github.com/fcakyon/content-moderation-deep-learning. Comment: Published at ICASSP 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.16338 Zobrazit plný text záznamu View this record from Arxiv