State-of-the-Art in Nudity Classification: A Comparative Analysis
This work tackles the problem of improving image classification for content moderation on online platforms, but it is incremental as it primarily reviews and compares existing methods.
This paper conducted a comparative analysis of nudity classification techniques, including CNN-based models and vision transformers, to address content moderation, finding limitations in current evaluation datasets and emphasizing the need for more diverse datasets.
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.