Fraud proofs enable any participant in the network to provide evidence of fraudulent behavior by other participants. This evidence can then be verified by other nodes on the network without requiring them to redo all the computations from scratch. >[!note] >When a fraud proof is submitted and validated successfully, penalties like [[Slashing|slashing]] can be imposed on [[Validators|validators]] confirming blocks for the network. The penalties vary depending on the specific blockchain protocol but typically involve slashing a portion of their stake or mining rewards. Slashing refers to reducing or confiscating some portion of a validator's stake as punishment for misbehavior. The use of fraud proofs helps deter malicious actors from attempting fraudulent activities within the blockchain system since they risk losing their staked funds if caught. It also enhances transparency and accountability within decentralized networks by allowing anyone to challenge suspicious behavior with evidence. Implementing fraud proofs requires careful design considerations and often involves cryptographic techniques like Zero Knowledge Proofs (ZKP) or Merkle Trees for efficient verification. A Merkle tree is a binary hash tree where each leaf node represents a transaction, and each non-leaf node is the hash of its child nodes' hashes. By including the root hash of this tree in each block header, it becomes possible to efficiently prove the inclusion or absence of specific transactions within that block. >[!example] >1. Validator A includes an invalid transaction in their proposed block. >2. Validator B detects this invalid transaction and wants to submit a fraud proof. >3. Validator B constructs a compact proof by providing only the necessary information without revealing all details about other transactions. >4. This compact proof typically consists of partial Merkle paths from relevant transactions up to the root hash included in the block header. >5. Other validators can then verify this proof by reconstructing parts of the Merkle path using available information (e.g., transaction data stored locally). >6. If validation succeeds, indicating fraudulent behavior, penalties can be imposed on validator A.