# Decentralized Image Verification Service

### **Overview**

The **Decentralized Image Verification Service (DIVS)** is a trustless, network-driven platform designed to 

* verify the authenticity of the claims referred in images shared across the internet. 
* Spot celebrities in photos
* Identify deepfakes 
* Describe an image
* Summarize a video
* and more..

Using a distributed network of inference providers running advanced **Vision-Language Models (VLMs)**, DIVS allows developers, fact-checkers, and applications to request independent, verifiable assessments of whether an image supports a given claim—without relying on a **single centralized authority.**

By leveraging the power of **decentralized computation and VLMs, consensus mechanisms, and backed by EigenLayer's crypto-economic security**, DIVS provides a censorship-resistant and scalable foundation for truth verification in visual content.

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### **What It Solves**

The internet is flooded with images that contain **textual claims, captions, or embedded information**, often spreading **misinformation** rapidly. Today, verifying these claims is:

* **Centralized:** Controlled by a few big platforms or organizations.
* **Opaque:** Lacking transparency in how decisions are made.
* **Slow:** Manual fact-checking cannot keep pace with viral image content.

DIVS solves this by creating a **trustless verification marketplace** where:

* Anyone can **submit an image and a textual claim** to verify.
* Independent node operators run **VLMs to analyze the image** and return factual verdicts.
* A **consensus score** is derived from multiple independent model results. (In Progress)
* Builders get a **fast, transparent, and verifiable API response**, usable in apps, bots, and extensions.

This creates an **open and scalable truth layer for the visual web.**

***

### **How It Works (High-Level)**

1. **Task Submission:** A Builder (developer) calls the DIVS API with an image and a claim to verify.
2. **Task Distribution:** The request is sent to a **decentralized network of Node Runners (aka Watchtowers)** running open VLMs.
3. **Parallel Processing:** Each node analyzes the image independently and submits a verification verdict with confidence scores.
4. **Consensus Engine:** The system aggregates responses, filters out malicious actors, and computes a **final trust score and verdict**. (Work in Progress)
5. **Result Delivery:** The Builder receives a **JSON response** containing verdict, confidence, and references (if available).

This design ensures **speed, transparency, and neutrality**—no single entity controls truth verification.

***

### **Who Should Read This**

* **🔹 Builders (API Consumers)** – Developers building apps, bots, extensions, and platforms that need **automated claim verification** for images. If you want to integrate truth-checking into your products, start here.
* **🔹 Node Runners (Compute Providers)** – Individuals or organizations operating GPU-enabled infrastructure willing to run **DIVS VLM nodes** and contribute to a **trustless verification network**.

Both groups are essential to the ecosystem: Builders generate demand, Node Runners supply decentralized computation.

***

### **Quick Links**

* 🚀 [**Get Started for Builders →**](https://docs.witnesschain.com/infinity-watch/proof-of-model-testnet/for-builders)
* ⚡ [**Get Started for Node Runners →**](https://docs.witnesschain.com/infinity-watch/proof-of-model-testnet/for-node-runners)