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Applying deep learning to classify pornographic images and videos

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arxiv 1511.08899 v1 pith:GOZWNG6Z submitted 2015-11-28 cs.CV cs.MMcs.NE

classification cs.CVcs.MMcs.NE
keywords imagesmethodsbuildclassifierdeepfeaturesincludingisolate
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It is no secret that pornographic material is now a one-click-away from everyone, including children and minors. General social media networks are striving to isolate adult images and videos from normal ones. Intelligent image analysis methods can help to automatically detect and isolate questionable images in media. Unfortunately, these methods require vast experience to design the classifier including one or more of the popular computer vision feature descriptors. We propose to build a classifier based on one of the recently flourishing deep learning techniques. Convolutional neural networks contain many layers for both automatic features extraction and classification. The benefit is an easier system to build (no need for hand-crafting features and classifiers). Additionally, our experiments show that it is even more accurate than the state of the art methods on the most recent benchmark dataset.

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  1. Advancing Content Moderation: Evaluating Large Language Models for Detecting Sensitive Content Across Text, Images, and Videos

    cs.CV 2024-11 conditional novelty 4.0 of 10

    A broad benchmark finds that general-purpose LLMs often outperform dedicated moderation APIs and prior CNN/LSTM baselines on text, image, and video content detection.

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