# 3️⃣ Fraud Proof Design

The fraud proof system in Axonum is built on a core principle of separating the fraud proof processes between Geth and opML. This design ensures a robust and efficient fraud proof mechanism. Here's a breakdown of the fraud proof system and our separation design:

1. **Fraud Proof System Overview:**
   * The fraud proof system is a critical component that guarantees the security and integrity of transactions on the Axonum optimistic rollup Layer 2.
   * It involves the verification of transactions and computations to ensure that any malicious behavior or inaccuracies are detected and addressed.
2. **Separation of Fraud Proof Processes:**
   * **Geth Fraud Proof Process:**
     * Geth, responsible for the Ethereum client on layer 2, handles the initial stages of fraud proof related to transaction validation and basic protocol adherence.
     * It verifies the correctness of transactions and ensures that they comply with the rules and protocol of the layer 2 system.
   * **opML Fraud Proof Process:**
     * opML, the Optimistic Machine Learning system integrated with Axonum, takes charge of the more intricate aspects of fraud proof related to machine learning model execution.
     * It verifies the correctness of machine learning computations and ensures the integrity of AI-related processes within the layer 2 framework.
3. **Benefits of Separation Design:**
   * **Enhanced Efficiency:**
     * By distributing the fraud proof responsibilities, we optimize the efficiency of the overall system. Geth focuses on transactional aspects, while opML handles ML-specific fraud proofs.
   * **Scalability:**
     * The separation design allows for scalability, enabling each component to independently scale based on its specific processing requirements.
   * **Flexibility:**
     * This separation provides flexibility for upgrades and improvements in either the Geth or opML components without compromising the entire fraud proof system.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.axonum.io/about/fraud-proof.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.