# Safe Image Moderation (`marielise.dev/safe-image-moderation`) Actor Screen images for adult, violent, racy, medical, and spoof content. Returns confidence scores for each category. Bring your own API key, pay only $0.01 per image. Perfect for UGC apps, e-commerce, social platforms, and ad networks. - **URL**: https://apify.com/marielise.dev/safe-image-moderation.md - **Developed by:** [Marielise](https://apify.com/marielise.dev) (community) - **Categories:** AI, Developer tools, SEO tools - **Stats:** 4 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Image Content Moderation AI-powered image content moderation using vision-capable LLMs. Analyze images for inappropriate content with high accuracy and get detailed confidence scores in seconds. ### Why Use This Actor? - **Multi-category detection**: Screens for adult, violence, racy, medical, and spoof content in one API call - **Multi-provider support**: Choose from OpenAI GPT-4o, Claude, Gemini, or Groq models - **Cost-effective**: Process 1,000 images for ~$3-5 using GPT-4o-mini - **Fast**: Average processing time of 2-5 seconds per image - **Transparent pricing**: Pay only for what you use with detailed token tracking - **No training required**: Works out of the box with configurable thresholds ### Features Analyzes images for inappropriate content across five categories: | Category | Description | Default Threshold | |----------|-------------|-------------------| | **Adult** | Explicit adult/NSFW content | 1.0 (strict) | | **Violence** | Violent or graphic content | 1.0 (strict) | | **Racy** | Suggestive but not explicit content | 1.0 (strict) | | **Medical** | Medical or graphic medical imagery | 0.5 (moderate) | | **Spoof** | Memes, edited, or manipulated images | 0.5 (moderate) | Returns: - Boolean `safe` verdict - Confidence scores (0-1) for each category - List of flagged categories - Token usage and cost tracking ### Quick Start #### 1. Set up API keys Add your LLM provider API key(s) in the Actor's Environment Variables: - `OPENAI_API_KEY` for OpenAI models - `ANTHROPIC_API_KEY` for Anthropic/Claude models - `GOOGLE_API_KEY` for Google/Gemini models - `GROQ_API_KEY` for Groq models #### 2. Run the Actor **Via Apify Console:** ```json { "imageUrl": "https://example.com/photo.jpg", "model": "openai:gpt-4o" } ```` **Via Apify API (cURL):** ```bash curl -X POST "https://api.apify.com/v2/acts/YOUR_USERNAME~image-content-moderation/runs?token=YOUR_TOKEN" \ -H "Content-Type: application/json" \ -d '{"imageUrl": "https://example.com/photo.jpg", "model": "openai:gpt-4o"}' ``` **Via Node.js:** ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_TOKEN' }); const run = await client.actor('YOUR_USERNAME/image-content-moderation').call({ imageUrl: 'https://example.com/photo.jpg', model: 'openai:gpt-4o', }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items[0]); // Moderation result ``` **Via Python:** ```python from apify_client import ApifyClient client = ApifyClient('YOUR_TOKEN') run = client.actor('YOUR_USERNAME/image-content-moderation').call(run_input={ 'imageUrl': 'https://example.com/photo.jpg', 'model': 'openai:gpt-4o', }) items = client.dataset(run['defaultDatasetId']).list_items().items print(items[0]) # Moderation result ``` ### Input Parameters | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `imageUrl` | string | One of | - | Public URL of image to analyze (JPEG, PNG, GIF, WebP, BMP) | | `imageBase64` | string | One of | - | Base64-encoded image data (max 20MB) | | `model` | string | No | `openai:gpt-4o` | LLM model in `provider:model` format | | `config` | object | No | See below | Override thresholds per category (0-1) | #### Supported Models | Provider | Model | Speed | Cost | Best For | |----------|-------|-------|------|----------| | OpenAI | `openai:gpt-4o` | Medium | $$ | Best accuracy | | OpenAI | `openai:gpt-4o-mini` | Fast | $ | Cost-effective | | Google | `google:gemini-2.0-flash` | Fast | $ | Speed | | Google | `google:gemini-1.5-pro` | Medium | $$ | Accuracy | | Anthropic | `anthropic:claude-sonnet-4-20250514` | Medium | $$ | Nuanced analysis | | Groq | `groq:meta-llama/llama-4-scout-17b-16e-instruct` | Fastest | $ | High throughput | #### Custom Thresholds Override thresholds per category (0-1 scale, higher = stricter): ```json { "imageUrl": "https://example.com/photo.jpg", "model": "openai:gpt-4o-mini", "config": { "adult": 0.3, "violence": 0.5, "racy": 0.7, "medical": 0, "spoof": 0.5 } } ``` Set to `0` to skip that category entirely. ### Output Format #### Safe Image ```json { "safe": true, "flagged": [], "scores": { "adult": 0.05, "violence": 0.02, "racy": 0.15, "medical": 0.01, "spoof": 0.08 }, "imageUrl": "https://example.com/photo.jpg", "modelUsed": "openai:gpt-4o", "configApplied": { "adult": 1, "violence": 1, "racy": 1, "medical": 0.5, "spoof": 0.5 }, "processedAt": "2024-12-21T10:30:00.000Z", "usage": { "tokens": { "input": 1250, "output": 85, "total": 1335 }, "cost": 0.003825 } } ``` #### Flagged Image ```json { "safe": false, "flagged": ["adult", "racy"], "scores": { "adult": 0.92, "violence": 0.03, "racy": 0.78, "medical": 0.01, "spoof": 0.05 }, "imageUrl": "https://example.com/flagged-photo.jpg", "modelUsed": "openai:gpt-4o", "processedAt": "2024-12-21T10:31:00.000Z", "usage": { "tokens": { "input": 1250, "output": 95, "total": 1345 }, "cost": 0.003875 } } ``` ### Use Cases #### User-Generated Content (UGC) Platforms Filter uploads before publishing to protect your community and comply with platform guidelines. #### E-commerce Product Images Validate product photos meet marketplace standards and prevent policy violations that could result in account suspension. #### Social Media Management Automatically screen content before posting across platforms to maintain brand safety. #### Ad Network Compliance Ensure advertising creative meets network policies before campaign launch. #### Media Library Management Bulk classify and tag images in media asset management systems. #### Dating App Photo Moderation Screen user profile photos and messages for inappropriate content. ### Pricing This Actor uses a **Pay Per Result** model. You pay for successful moderation results based on the underlying LLM costs plus a small platform fee. **Estimated costs per 1,000 images:** - GPT-4o-mini: ~$3-5 - GPT-4o: ~$15-20 - Gemini Flash: ~$2-4 - Groq Llama: ~$1-2 Actual costs depend on image complexity and token usage. Each result includes detailed `usage` data for full cost transparency. ### FAQ #### What image formats are supported? JPEG, PNG, GIF, BMP, and WebP. Images must be under 20MB. #### Do I need multiple API keys? No, you only need an API key for the provider you want to use. Most users start with OpenAI. #### How accurate is the moderation? Accuracy depends on the model used. GPT-4o and Claude Sonnet achieve high accuracy on standard benchmarks. Results include confidence scores so you can set appropriate thresholds for your use case. #### Can I process multiple images at once? Currently, the Actor processes one image per run. For batch processing, use the Apify API to run multiple instances in parallel or integrate with a dataset crawler. #### What if my image URL requires authentication? Use the `imageBase64` input field instead. Convert your image to base64 and pass it directly. #### How do I handle false positives/negatives? Adjust the threshold values in the `config` parameter. Lower thresholds make detection more sensitive (fewer false negatives, more false positives). Higher thresholds are more permissive. ### API Keys Setup 1. **OpenAI**: [platform.openai.com/api-keys](https://platform.openai.com/api-keys) 2. **Anthropic**: [console.anthropic.com](https://console.anthropic.com) 3. **Google AI**: [aistudio.google.com/apikey](https://aistudio.google.com/apikey) 4. **Groq**: [console.groq.com](https://console.groq.com) Add your API key(s) to the Actor's **Environment Variables** section in the Apify Console. ### Limitations - Maximum image size: 20MB - Image must be publicly accessible (for URL input) or base64-encoded - Processing time varies by model and image complexity (typically 2-10 seconds) - Rate limits depend on your LLM provider plan ### Support For issues or feature requests, please contact the Actor developer or leave a review on the Apify Store. # Actor input Schema ## `imageUrl` (type: `string`): Public URL of the image to moderate (JPEG, PNG, GIF, WebP, BMP). Must be publicly accessible without authentication. ## `imageBase64` (type: `string`): Base64-encoded image data. Use this for images not accessible via URL. Maximum size: 20MB. Provide either imageUrl OR imageBase64, not both. ## `model` (type: `string`): Vision-capable LLM model. Requires corresponding API key in environment variables (OPENAI\_API\_KEY, ANTHROPIC\_API\_KEY, GOOGLE\_API\_KEY, or GROQ\_API\_KEY). ## `config` (type: `object`): Override detection thresholds (0-1). Higher values = stricter filtering. Set to 0 to skip a category entirely. ## Actor input object example ```json { "imageUrl": "https://example.com/photo.jpg", "model": "openai:gpt-4o", "config": { "adult": 1, "violence": 1, "racy": 1, "medical": 0.5, "spoof": 0.5 } } ``` # Actor output Schema ## `moderationResults` (type: `string`): Dataset containing moderation results with boolean safe verdict, array of flagged categories, confidence scores (0-1) for adult/violence/racy/medical/spoof, model used, processing timestamp, and token usage with cost ## `keyValueStore` (type: `string`): Actor run key-value store for accessing run metadata # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "imageUrl": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png", "model": "openai:gpt-4o", "config": { "adult": 1, "violence": 1, "racy": 1, "medical": 0.5, "spoof": 0.5 } }; // Run the Actor and wait for it to finish const run = await client.actor("marielise.dev/safe-image-moderation").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "imageUrl": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png", "model": "openai:gpt-4o", "config": { "adult": 1, "violence": 1, "racy": 1, "medical": 0.5, "spoof": 0.5, }, } # Run the Actor and wait for it to finish run = client.actor("marielise.dev/safe-image-moderation").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "imageUrl": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png", "model": "openai:gpt-4o", "config": { "adult": 1, "violence": 1, "racy": 1, "medical": 0.5, "spoof": 0.5 } }' | apify call marielise.dev/safe-image-moderation --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=marielise.dev/safe-image-moderation", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Safe Image Moderation", "description": "Screen images for adult, violent, racy, medical, and spoof content. Returns confidence scores for each category. Bring your own API key, pay only $0.01 per image. Perfect for UGC apps, e-commerce, social platforms, and ad networks.", "version": "0.0", "x-build-id": "6uZ4RxW7cOOTXJMiO" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/marielise.dev~safe-image-moderation/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-marielise.dev-safe-image-moderation", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/marielise.dev~safe-image-moderation/runs": { "post": { "operationId": "runs-sync-marielise.dev-safe-image-moderation", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/marielise.dev~safe-image-moderation/run-sync": { "post": { "operationId": "run-sync-marielise.dev-safe-image-moderation", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "imageUrl": { "title": "Image URL", "pattern": "^https?://.+", "type": "string", "description": "Public URL of the image to moderate (JPEG, PNG, GIF, WebP, BMP). Must be publicly accessible without authentication." }, "imageBase64": { "title": "Image Base64", "type": "string", "description": "Base64-encoded image data. Use this for images not accessible via URL. Maximum size: 20MB. Provide either imageUrl OR imageBase64, not both." }, "model": { "title": "Model", "enum": [ "openai:gpt-4o", "openai:gpt-4o-mini", "openai:gpt-4-turbo", "anthropic:claude-sonnet-4-20250514", "anthropic:claude-3-5-sonnet-20241022", "google:gemini-2.0-flash", "google:gemini-1.5-pro", "groq:meta-llama/llama-4-scout-17b-16e-instruct" ], "type": "string", "description": "Vision-capable LLM model. Requires corresponding API key in environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY, or GROQ_API_KEY).", "default": "openai:gpt-4o" }, "config": { "title": "Moderation Thresholds", "type": "object", "description": "Override detection thresholds (0-1). Higher values = stricter filtering. Set to 0 to skip a category entirely.", "properties": { "adult": { "title": "Adult Threshold", "type": "number", "description": "Threshold for adult/NSFW content (0-1). Default: 1 (very strict)", "minimum": 0, "maximum": 1, "default": 1 }, "violence": { "title": "Violence Threshold", "type": "number", "description": "Threshold for violent/graphic content (0-1). Default: 1 (very strict)", "minimum": 0, "maximum": 1, "default": 1 }, "racy": { "title": "Racy Threshold", "type": "number", "description": "Threshold for racy/suggestive content (0-1). Default: 1 (very strict)", "minimum": 0, "maximum": 1, "default": 1 }, "medical": { "title": "Medical Threshold", "type": "number", "description": "Threshold for medical/graphic imagery (0-1). Default: 0.5 (moderate)", "minimum": 0, "maximum": 1, "default": 0.5 }, "spoof": { "title": "Spoof Threshold", "type": "number", "description": "Threshold for meme/edited content detection (0-1). Default: 0.5 (moderate)", "minimum": 0, "maximum": 1, "default": 0.5 } } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```