C4Censor: A Lightweight Benchmark Dataset for Inappropriate Content Detection

<b>C4Censor</b> is a multi-class image benchmark for fine-grained content moderation across four categories: Blood &amp; Gore, Pornography, Terrorism, and Neutral. Each category contains three challenging subclasses (e.g., Hentai vs. Anime, Counter-Terrorism vs. War-Crimes), with 500 images per subclass totaling 6,000 manually annotated images.This dataset addresses limitations of existing binary classification benchmarks by providing a unified, balanced challenge for real-world moderation tasks. Benchmark evaluations with state-of-the-art models (ViT, Xception, CAiT) achieved maximum accuracy of 62.13%, highlighting the dataset's complexity.<b><i>"This dataset contains graphic, violent, and sexually explicit imagery collected for academic research purposes only. Users must agree to ethical usage terms before access."</i></b><b>Manuscript</b>: Submitted to Journal of Computational Social Science<b>Supplementary Materials</b>: Includes visual examples and misclassification instances from the dataset (not included in the manuscript for JCSO ethical compliance)