NSFW-Aware AI Moderation vs Google Vision / AWS Rekognition Technical · Deep-diveNSFW-aware AI moderation vsGoogle Vision · AWS RekognitionAn adult platform that wires Google Vision or AWS Rekognition into the moderation pipeline gets the same answer on every image: "unsafe". The Big-Tech APIs are trained on "is there nudity, yes/no" — on an adult platform the answer is yes by definition. The questions that actually matter — is this CSAM, is this under-18 likely, is this non-consent, is this escort spam — are not even in the model. This is the comparison.~100%false-positive on legal-adult (Big-Tech)"refuse"on CSAM-class detection (Big-Tech)0.3%FPR on purpose-built99.7%CSAM accuracy The fundamental problemWhy "is there nudity" is the wrong questionGoogle Vision's SafeSearch and AWS Rekognition's content-moderation labels are designed for general-purpose platforms where ANY adult content is to be removed. The model returns a probability over labels like "explicit nudity", "suggestive", "violence". On an adult site — where all content is, by definition, adult — the probability is essentially 1.0 for every image. The signal-to-noise ratio is zero.The actually-useful questions are different:Is this CSAM? — the most important moderation question on adult platforms by far. Big-Tech APIs explicitly refuse to detect.Is the subject potentially under-18? — the second-most-important. Big-Tech doesn't model this; their pipeline flags adult-period and stops.Are there non-consent indicators? — restraint without disclosure, distress signals, struggle patterns. Big-Tech can't see these.Is this escort-spam or legit creator content? — visually distinguishable but Big-Tech doesn't have the training data.Is this AI-generated? — under Texas SB12 and EU AI Act, AI-generated likeness theft is a separate category. Big-Tech detects AI-generation as a feature but doesn't tie it to consent. Per-dimension comparisonBig-Tech APIs vs purpose-built moderationDimensionGoogle Vision / AWS RekognitionPurpose-built (Hive / Sightengine / ours)Legal-adult content~100% false-positive ("unsafe")~0.3% false-positive (passes with risk score)CSAM detectionRefused in TOS; "we don't process adult"99.7% accuracy + auto-report to NCMEC / IWFAge-suspicion (under-18)Not modeled (folded into "adult-period")Age-estimate classifier with documented FNRNon-consent indicatorsNot modeledTrained on indicator patterns; flags borderlineEscort-spam vs creator contentCannot distinguish~94% accuracy classifierAI-generated detectionYes (feature-level)Yes + likeness-theft (consent-tied)Per-platform tuningNoneThreshold + categories configurable per API keyDeployment optionsCloud onlyCloud OR on-prem (zero content leak) Cost realityWhat this actually costs at 1M images/monthSolutionCost per 1M imagesCSAM auto-reportOn-prem optionGoogle Vision SafeSearch$1,500 (per-image $0.0015)NoNoAWS Rekognition content-mod$1,000 (per-image $0.001)NoNoHive Moderation~$800 (per-image $0.0008)PartialNoSightengine~$2,000 (subscription)No (per TOS)Noadults.dev AI Moderation API (planned)~$800 (per-image $0.0008)Yes — NCMEC + IWF + Stop CSAMYes ($5K/mo on-prem)For a 10M-images/month platform, the per-image fee difference is meaningful but not decisive. The decisive factors are CSAM auto-report (legally protective) and on-prem deployment (regulatory de-risking). ImplementationThe 4-modality patternModern adult-platform moderation pipelines run four classifiers in parallel:🖼️Image (still)CSAM / age-suspicion / non-consent classifiers. Per-image inference latency < 150ms p95. Routed before publication.🎬Video (frame-extract)Sampled frames (every 2 seconds + scene-change). Same classifiers. Per-minute cost dominates.💬Text (chat / DM)Grooming-pattern detection, escort-spam, prompt-injection on bot DMs. Lighter classifier; per-message $0.0001.🎙️Audio (cam)Distress detection in live audio streams. Less mature than image; deploy with human review queue. Where we fitadults.dev validation productsAI Moderation API →4-modality NSFW-aware moderation. CSAM auto-report. On-prem option. 100K calls/mo free for year-1 early-access (first 50 platforms).Persona Engine →White-label AI companion with the same content classification built into the chat layer — you don't have to bolt moderation on top. FAQCommon questionsWhy doesn't Google just unlock the adult-content detection?Liability + brand. Google's TOS prohibits use of Vision API for adult-platform moderation. Even if their model could detect, the contractual block means it can't be relied on.What about local LLMs (Llama-Guard)?Llama-Guard 3 covers text. For image / video the adult-specific classifiers don't yet exist in open-source. The "DIY" path requires building your own training dataset, which is its own compliance problem.Do I need 4 modalities from day 1?No. Image is the priority. Video next (cost-significant). Text and audio are optional unless you have user-to-user chat or cam-audio specifically.CSAM false-positives — how do you handle?Dual-review: classifier flags, human reviewer confirms, only then auto-report to NCMEC. Single-classifier auto-reporting causes more harm than it prevents.Will this satisfy CCBill / Verotel requirements?Yes — both processors' merchant TOS explicitly accept third-party moderation. Compliance is at the platform level, not the moderation-vendor level.What happens if the moderation API is down?Production pipelines fail-closed: content does not publish until moderation passes. Down moderation = backlog, not bypass. RelatedKeep readingUK OSA compliance guide →Compliance API →DMCA + Brand Protection →