State-of-the-Art in Nudity Classification: A Comparative Analysis
Fatih Cagatay Akyon Alptekin Temizel
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.