Latent Space Probing for Adult Content Detection in Video Generative Models
The rapid proliferation of AI-powered video generation systems has introduced significant challenges in content moderation, particularly with respect to adult and sexually explicit material. Existing detection methods operate on either prompts or decoded pixel-space outputs. Therefore, both approaches are blind to the rich internal representations formed during generation. In this paper, we propose a novel latent space probing framework that intercepts the denoised latent representations produced by the CogVideoX video diffusion model during inference and attaches lightweight classifiers to perform real-time adult content detection. To support this work, we construct a large-scale binary ...
| Authors | Alizishaan Khatri, Chiquita Prabhu |
|---|---|
| Primary category | cs.CV |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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