Best NSFW Detection APIs in 2026

import requests

response = requests.post(
    "https://api.thehive.ai/api/v2/task/sync",
    headers={"Authorization": "Token YOUR_API_KEY"},
    json={"url": "https://example.com/image.jpg"}
)
result = response.json()
for cls in result["output"][0]["classes"]:
    if cls["score"] > 0.8:
        print(f"{cls['class']}: {cls['score']:.3f}")

import boto3

client = boto3.client("rekognition")
response = client.detect_moderation_labels(
    Image={"S3Object": {"Bucket": "my-bucket", "Name": "image.jpg"}},
    MinConfidence=70
)
for label in response["ModerationLabels"]:
    print(f"{label['Name']}: {label['Confidence']:.1f}%")
    if label.get("ParentName"):
        print(f"  Parent: {label['ParentName']}")

from google.cloud import vision

client = vision.ImageAnnotatorClient()
image = vision.Image()
image.source.image_uri = "gs://my-bucket/image.jpg"

response = client.safe_search_detection(image=image)
safe = response.safe_search_annotation
print(f"Adult: {safe.adult.name}")
print(f"Violence: {safe.violence.name}")
print(f"Racy: {safe.racy.name}")

from azure.ai.contentsafety import ContentSafetyClient
from azure.core.credentials import AzureKeyCredential

client = ContentSafetyClient(
    "https://<resource>.cognitiveservices.azure.com",
    AzureKeyCredential("<key>")
)
from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData
request = AnalyzeImageOptions(image=ImageData(url="https://example.com/img.jpg"))
response = client.analyze_image(request)
for cat in response.categories_analysis:
    print(f"{cat.category}: severity {cat.severity}")

import requests

params = {
    "models": "nudity-2.1,weapon,gore",
    "api_user": "YOUR_USER",
    "api_secret": "YOUR_SECRET",
    "url": "https://example.com/image.jpg"
}
r = requests.get("https://api.sightengine.com/1.0/check.json", params=params)
result = r.json()
print(f"Nudity: {result['nudity']['sexual_activity']}")
print(f"Weapon: {result['weapon']}")

from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc

channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)
metadata = (("authorization", "Key YOUR_PAT"),)

request = service_pb2.PostModelOutputsRequest(
    model_id="moderation-recognition",
    inputs=[resources_pb2.Input(
        data=resources_pb2.Data(image=resources_pb2.Image(url="https://example.com/img.jpg"))
    )]
)
response = stub.PostModelOutputs(request, metadata=metadata)
for concept in response.outputs[0].data.concepts:
    print(f"{concept.name}: {concept.value:.3f}")

import boto3

bedrock = boto3.client("bedrock-runtime")
response = bedrock.apply_guardrail(
    guardrailIdentifier="my-guardrail-id",
    guardrailVersion="1",
    source="INPUT",
    content=[{"text": {"text": "User message to check"}}]
)
print(f"Action: {response['action']}")
for assessment in response.get("assessments", []):
    for policy in assessment.get("contentPolicy", {}).get("filters", []):
        print(f"  {policy['type']}: {policy['action']}")

from nudenet import NudeDetector

detector = NudeDetector()
detections = detector.detect("path/to/image.jpg")
for d in detections:
    print(f"{d['class']}: {d['score']:.3f}")
    print(f"  Box: {d['box']}")
    if d["score"] > 0.6:
        print("  -> Flagged for review")

from openai import OpenAI

client = OpenAI()
response = client.moderations.create(
    model="omni-moderation-latest",
    input="Text to check for policy violations"
)
result = response.results[0]
if result.flagged:
    for cat, flagged in result.categories.dict().items():
        if flagged:
            score = getattr(result.category_scores, cat)
            print(f"{cat}: {score:.4f}")

from transformers import pipeline

classifier = pipeline(
    "image-classification",
    model="Falconsai/nsfw_image_detection"
)
result = classifier("path/to/image.jpg")
for label in result:
    print(f"{label['label']}: {label['score']:.4f}")

import requests

response = requests.get("https://im-api1.webpurify.com/services/rest/", params={
    "api_key": "YOUR_API_KEY",
    "method": "webpurify.aim.imgcheck",
    "imgurl": "https://example.com/image.jpg",
    "format": "json"
})
result = response.json()
nudity_score = result["rsp"]["nudity"]
print(f"Nudity score: {nudity_score}")
if float(nudity_score) > 70:
    print("Flagged, escalating to human review")