[2605.00874] Latent Space Probing for Adult Content Detection in Video Generative Models
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.00874 (cs)
[Submitted on 25 Apr 2026]
Title:Latent Space Probing for Adult Content Detection in Video Generative Models
Authors:Alizishaan Khatri, Chiquita Prabhu
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Abstract: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 dataset of 11039 ten-second video clips (5086 violating, 5953 non-violating) sourced from adult websites and YouTube respectively. We introduce two lightweight probing classifier architectures. We train and evaluate it on the dataset. Our work demonstrates that latent-space signals encode strong discriminative features for harmful content detection, achieving 97.29% F1 on our held-out test set with an overhead in the 4-6ms range. Our results suggest that probing the latent space results in improvements in both detection performance as well as cost.
Comments: To be published in 2026 56th Annual IEEE International Conference on Dependable Systems and Networks Workshops (DSN-W)
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
Cite as: arXiv:2605.00874 [cs.CV]
(or arXiv:2605.00874v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.00874
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Submission history
From: Alizishaan Khatri [view email] [v1] Sat, 25 Apr 2026 01:01:02 UTC (1,021 KB)
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