This article presents a technical overview of a multimodal AI framework that combines image and audio analysis to identify pornographic video content, detailing model architectures, feature extraction methods, and experimental results achieving 93.4% accuracy on a 3,000‑sample test set.
This article explains a deep‑learning pipeline that preprocesses audio, extracts FBank features, applies SpecAugment, and uses a CNN‑BI‑LSTM‑Attention model to automatically identify pornographic and ASMR speech for content moderation.
This article describes Meituan's large‑scale AI‑driven image moderation platform, detailing deep‑learning based watermark detection, celebrity face recognition, pornographic image detection, and scene classification techniques, along with system architecture, data preparation, model evaluation, and deployment considerations.
This article explains the challenges of image‑based porn detection, details the multi‑label classification approach of the Wukong system, and reveals the deep‑learning techniques—including CNN evolution, transfer learning, loss functions, adversarial training, and GAN‑based data augmentation—that enable over 99.5% accuracy with massive daily request volumes.
