# Watermark & Logo Detector (`robin.geekydev/watermark-logo-detector`) Actor

LLM-powered detector for watermarks, logos, stamps, corner marks, and text overlays in images.

- **URL**: https://apify.com/robin.geekydev/watermark-logo-detector.md
- **Developed by:** [Robin p](https://apify.com/robin.geekydev) (community)
- **Categories:** AI, Automation, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 1,000 detected images

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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Watermark & Logo Detector

Detect watermarks, logos, stamps, corner marks, and text overlays in images with an LLM. Useful for stock-photo screening, UGC rights checks, and catalog cleanup.

### What it does

For each image URL (or base64 payload), the Actor returns:

| Field | Meaning |
|---|---|
| `hasOverlay` | Any overlay found |
| `hasWatermark` | Watermark / stamp / corner mark |
| `hasLogo` | Brand or company logo overlay |
| `hasTextOverlay` | Burned-in caption / promo text |
| `detections[]` | Per-overlay type, label, text, position, opacity, confidence |
| `primaryType` | Highest-confidence overlay type (`none` if clean) |
| `reason` | Short explanation |

Detection types: `watermark`, `logo`, `stamp`, `text_overlay`, `corner_mark`.

### Pricing

This Actor uses **pay per result**:

- **$0.01 per successfully analyzed image** (`$10.00 / 1,000 results`)
- You only pay for successful results written to the default dataset
- Failed images are not charged (logged in the errors dataset)

### How to use

1. Open this Actor on Apify
2. Paste one or more public image URLs (or provide base64 in `images`)
3. Optionally set `minConfidence` (default `0.5`)
4. Click **Start**
5. Review results in the **Dataset** / **Output** tab

#### Input

| Field | Required | Description |
|---|---|---|
| `imageUrls` | one of urls/images | Public image URLs |
| `images` | one of urls/images | Objects: `{ id?, url?, base64? }` |
| `minConfidence` | no | Ignore weak detections (0–1, default `0.5`) |
| `maxConcurrency` | no | Parallel analyses (default `5`) |

#### Example input

```json
{
  "imageUrls": [
    "https://example.com/photo-1.jpg",
    "https://example.com/photo-2.jpg"
  ],
  "minConfidence": 0.5,
  "maxConcurrency": 5
}
```

#### Example dataset item

```json
{
  "imageId": "url-1",
  "imageUrl": "https://example.com/photo-1.jpg",
  "hasOverlay": true,
  "hasWatermark": true,
  "hasLogo": false,
  "hasTextOverlay": false,
  "detectionCount": 1,
  "primaryType": "watermark",
  "overallConfidence": 0.91,
  "detections": [
    {
      "type": "watermark",
      "label": "Stock watermark",
      "text": "sample",
      "position": "repeated",
      "opacity": "medium",
      "confidence": 0.91,
      "isBrandLogo": false
    }
  ],
  "reason": "Repeated semi-transparent stock watermark across the image.",
  "error": null
}
```

### Notes

- Image URLs must be publicly reachable so the Actor can download them for analysis.
- For private images, pass `base64` in the `images` array instead of a URL.
- Normal scene text (e.g. a real storefront sign) is not treated as an overlay.
- LLM inference cost is covered by the Actor configuration; Apify run compute is billed by Apify.

# Actor input Schema

## `imageUrls` (type: `array`):

List of publicly reachable image URLs to analyze for watermarks and logos.

## `images` (type: `array`):

Optional structured image list. Each item can include url, base64, and an optional id.

## `minConfidence` (type: `number`):

Ignore detections below this confidence (0–1).

## `maxConcurrency` (type: `integer`):

How many images to analyze in parallel.

## Actor input object example

```json
{
  "imageUrls": [
    "https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=800"
  ],
  "images": [],
  "minConfidence": 0.5,
  "maxConcurrency": 5
}
```

# Actor output Schema

## `results` (type: `string`):

Successfully analyzed images with watermark/logo detections.

## `summary` (type: `string`):

Totals for succeeded, failed, watermarked, and logo-marked images.

## `errors` (type: `string`):

Images that could not be analyzed. These are not billed.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "imageUrls": [
        "https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=800"
    ],
    "images": [],
    "minConfidence": 0.5,
    "maxConcurrency": 5
};

// Run the Actor and wait for it to finish
const run = await client.actor("robin.geekydev/watermark-logo-detector").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "imageUrls": ["https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=800"],
    "images": [],
    "minConfidence": 0.5,
    "maxConcurrency": 5,
}

# Run the Actor and wait for it to finish
run = client.actor("robin.geekydev/watermark-logo-detector").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "imageUrls": [
    "https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=800"
  ],
  "images": [],
  "minConfidence": 0.5,
  "maxConcurrency": 5
}' |
apify call robin.geekydev/watermark-logo-detector --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,robin.geekydev/watermark-logo-detector"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/IvrNgjkezM6Dhai0r/builds/3WU4DHa1hSyocwjZx/openapi.json