--- title: "AI-assisted media classification" description: "NSFW, hard-nudity and soft-nudity signals with configurable thresholds, frame sampling, and review routing. Not age, identity, consent, or rights verification." url: "https://adultinfra.com/docs/ai/media-moderation/" dateModified: "2026-09-12" --- [Home](https://adultinfra.com/) / [Docs](https://adultinfra.com/docs/) / AI and GPU # AI-assisted media classification NSFW, hard-nudity and soft-nudity signals with configurable thresholds, frame sampling, and review routing. Not age, identity, consent, or rights verification. LAST REVIEWED 2026-09-13 ON THIS PAGE - [Signals](https://adultinfra.com/docs/ai/media-moderation/#signals) - [Configurable thresholds](https://adultinfra.com/docs/ai/media-moderation/#configurable-thresholds) - [Frame sampling](https://adultinfra.com/docs/ai/media-moderation/#frame-sampling) - [Routing to review](https://adultinfra.com/docs/ai/media-moderation/#routing-to-review) - [What this is not](https://adultinfra.com/docs/ai/media-moderation/#what-this-is-not) - [Human review remains necessary](https://adultinfra.com/docs/ai/media-moderation/#human-review-remains-necessary) - [Where AdultInfra fits](https://adultinfra.com/docs/ai/media-moderation/#where-adultinfra-fits) **AI-assisted media classification** scores images and video for content signals so a platform can route items for review, apply labels, or enforce its own policy at scale. It is a triage tool that narrows a human reviewer’s attention; it is not a decision-maker, and it does not verify anything about the people depicted. ## Signals Classification models emit probability-like scores for a set of content signals. A practical set for adult platforms: - **NSFW:** an aggregate score that the item contains explicit material. - **Hard nudity:** explicit genital or sexual content. - **Soft nudity:** partial or suggestive nudity without explicit content. - **Other labels:** violence, drugs, or platform-specific categories you define. Treat these as independent signals, not a single verdict. A high soft-nudity score with a low hard-nudity score is a different review outcome than both being high, and the customer’s policy decides which combinations are acceptable for which surface. ## Configurable thresholds A score becomes an action through a threshold, and thresholds belong to the customer. Typical bands: Score band Suggested action Below low threshold Auto-accept under the customer’s policy Between thresholds Route to human review Above high threshold Auto-flag for priority review or block per policy Thresholds should differ by surface: a public catalogue, a logged-in feed, and a private creator page can each tolerate different content. Make thresholds configurable per tenant and per surface, and log which threshold produced an action so a disputed decision can be explained. ## Frame sampling Video classification samples frames rather than scoring every frame. Sampling design drives both cost and accuracy: - **Even sampling** across the duration gives broad coverage cheaply. - **Scene-change sampling** concentrates on transitions, catching content that appears only briefly. - **Dense sampling** near flagged regions refines a borderline result. Too few frames miss short explicit scenes; too many raise cost without improving the verdict. The right strategy depends on the content type: a long-form scene and a short clip need different sampling. Record which frames were scored so a reviewer can jump to the evidence. ## Routing to review Classification output is only useful if it lands somewhere actionable. Route scored items into a review queue with: - The signal scores and the frames that produced them. - The threshold that triggered review and the surface’s policy. - Enough context for a reviewer to decide without re-watching the whole asset. Prioritise the queue by score confidence and by exposure: an item about to be promoted or surfaced publicly is more urgent than a dormant archive item. ## What this is not Be explicit about the boundary. Classification **does not** and cannot verify: - **Age:** it does not establish that anyone depicted is an adult. - **Identity:** it does not confirm who is depicted. - **Consent:** it does not establish that anyone consented. - **Rights:** it does not confirm ownership or licensing. These are verification and legal obligations that no content classifier satisfies. Presenting a NSFW score as if it were an age or consent check is both inaccurate and dangerous. Age assurance, consent records, and rights documentation are separate systems and separate responsibilities. ## Human review remains necessary Classification reduces the volume a human must inspect; it does not remove the need for review. Models drift, thresholds are imperfect, and adversarial uploads are designed to evade scoring. Keep human reviewers in the loop for anything routed above a threshold, retain an appeal path, and treat the model’s output as evidence rather than a verdict. ## Where AdultInfra fits AdultInfra runs AI-assisted classification with customer-configured thresholds and a clear boundary between content signals and verification. If you need to triage a growing upload queue without outsourcing your policy, start with [adult AI inference](https://adultinfra.com/adult-ai-inference/) and bring one content type and one surface. **Need this configured for your platform?** [Get a test plan](https://adultinfra.com/contact/) and an engineer will map the resource, cache, and delivery design to your workload. ```json {"@context":"https://schema.org","@graph":[{"@type":"TechArticle","headline":"AI-assisted media classification","description":"NSFW, hard-nudity and soft-nudity signals with configurable thresholds, frame sampling, and review routing. 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