Proof-of-AIGC Protocols: Securing AI Content

• Proof-of-AIGC protocols are cryptographically grounded, blockchain-anchored systems that securely record AI content creation, transfer, and rights management.
• They employ digital signatures, hash commitments, and atomic smart contracts across both permissioned and open networks to ensure data integrity and scalable auditability.
• Performance metrics indicate reduced response latency and efficient audit procedures, underscoring the protocols' viability for high-throughput, real-world AIGC applications.

Proof-of-AIGC protocols are cryptographically grounded, blockchain-anchored schemes enabling verifiable provenance, tamper-resistance, and copyright management for AI-generated content (AIGC) in decentralized networks. By algorithmically recording generation, transfer, and dispute processes, these protocols construct immutable, auditable proofs tying content artifacts and metadata to specific participants across their lifecycle. Key technical designs prominently rely on hash commitments, digital signatures, atomic exchanges, fraud-proof and copy-detection mechanisms, incentive structures, and advanced indexing schemes for scalable auditability (Liu et al., 2023, Jiang et al., 21 Jun 2024).

1. Core System Models and Architectural Paradigms

Proof-of-AIGC protocols involve multiple participant classes and operate in both permissioned and open blockchain environments. Edge-centric models utilize resource-constrained Producers (users issuing prompts and requesting content), decentralized Edge Service Providers (ESPs running AIGC models and participating as full blockchain nodes), Consumers (buyers/licensors of AIGC), and Attackers (entities attempting to tamper, duplicate, or plagiarize content). The blockchain network typically implements delegated Proof-of-Stake (DPoS) consensus for edge deployments (Liu et al., 2023) or a consortium chain based on Hyperledger Fabric with smart-contract chaincode for enterprise-grade, multi-party settings (Jiang et al., 21 Jun 2024).

World states are bound to content identifiers and encode tuples of model metadata, participant signatures, current owners, hash commitments, status flags, and a scalable membership structure such as an Indistinguishable Bloom Filter (IBF) for transaction indexing (Jiang et al., 21 Jun 2024). All mutable and immutable aspects of AIGC generation and transfer are thus cryptographically and logically bound to their originating and receiving actors.

2. Cryptographic Primitives, Transaction Structures, and Atomic Workflows

Protocols employ collision-resistant hash functions (such as SHA-256) to commit content artifacts: $C = H(I)$ where $I$ is the generated output. Digital signatures authenticate and bind participant actions: $\sigma = \text{Sign}_{sk}(m)$ with subsequent on-chain verification $\text{Verify}_{pk}(m, \sigma)$.

Each on-chain transaction is structured as $$TX = (\text{From}, \text{To}, \text{Payload}, t, \sigma)$$ and includes hash/time-based atomic lock constructs, such as Hash-Time-Lock (HTL) contracts (Liu et al., 2023). These smart contracts guarantee fair, atomic exchange of content and funds between Producers and ESPs or Consumers—a key property enforced by the sequential and verifiable release of secret preimages under deadline constraints.

Transaction event sets for any product $P$ are indexed by scalable IBF structures, supporting $O(1)$ retrieval and membership checks of valid transaction IDs during registration, generation, upload, transfer, and management phases (Jiang et al., 21 Jun 2024). This enables efficient auditability, particularly in dispute resolution.

3. Detailed Proof-of-AIGC Protocol Logic

3.1 Registration and Generation

Producers submit prompts (e.g., textual Args) and generation fees; ESPs model-infer the requested output, compute $C = H(I)$, and officially record on-chain: $$TX_{\text{reg}} := (\text{ESP}, \text{Producer}, (C, \text{meta}, T_{\text{chal}}), t, \sigma_{\text{ESP}})$$ (Liu et al., 2023, Jiang et al., 21 Jun 2024). For each interaction, Producers and Providers sign all principal artifacts and metadata (prompts, steps, seed, creation date), sequentially updating world-state records and inserting event IDs into the IBF (Jiang et al., 21 Jun 2024).

3.2 Atomic Exchanges and Trading

Trading and ownership transfers utilize HTL smart contracts. Both parties commit assets (content, funds) under cryptographic hash locks derived from random secrets $h = H(R)$. The exchange is released only on provision of $R$, preventing premature or malicious asset grabs and guaranteeing atomicity in all asset exchanges (Liu et al., 2023).

3.3 Copy-Detection and Challenge Mechanisms

For duplication or ownership tampering, a fraud-proof protocol is employed: a challenger Producer submits $(C_1, C_2, I_1, I_2, \Delta)$ (where $\Delta$ is a deposit) to a committee of ESP full nodes. These validate hash-preimage integrity and compute similarity metrics across multiple dimensions (histogram, perceptual-hash, difference-hash). The on-chain verification predicate $V_{\text{PoAIGC}}$ discerns duplication based on threshold crossings by these metrics. Upon affirmative detection, the duplicate is deregistered and deposits are disbursed accordingly; malicious collusion triggers collateral slashing for involved ESPs (Liu et al., 2023).

3.4 Lifecycle Recording and Auditable Provenance

AIGC-Chain captures every key event (registration, generation, uploading, trading, management) as a signed transaction, updating the content's world state and feeding event IDs into the IBF (Jiang et al., 21 Jun 2024). Auditors can retrieve full content provenance by (1) querying transaction IDs via IBF, (2) verifying signature chains, and (3) reviewing original prompts for legal compliance. Each transaction forms a cryptographic micro-proof of user or provider contribution.

4. Incentive, Reputation, and Governance Mechanisms

Protocols employ multi-stage incentive structures: ESPs stake collateral for block production (block rewards function $$R_{\text{blk}} = f(\Delta_{\text{ESP}}, \text{inflation\_target})$$ (Liu et al., 2023)), and both Producers and ESPs provision deposits for challenge and trading atomicity. These economic incentives ensure honest participation, timely execution, and discourage fraudulent dupe claims or asset grab attempts.

Reputation schemes are constructed using Multi-weight Subjective Logic (MWSL). Each Producer calculates local and recommended opinions $(b, d, u)$ over ESPs, aggregating them via subjective fusion operators. The reputation score $$R = b_{\text{fin}} + u_{\text{fin}} \cdot d_{\text{fin}}$$ governs ESP selection and balances workload distribution, optimally reducing response latency and matching actual service quality (Liu et al., 2023).

In consortium deployments, governance mechanisms include decentralized audition, endorsement policies, and channel confidentiality, aiming to reduce risks of auditor collusion or participant bribery (Jiang et al., 21 Jun 2024).

5. Security and Performance Properties

Proof-of-AIGC protocols guarantee integrity (immutability of provenance records), non-repudiation (mandatory digital signature for each lifecycle event), and auditability. The IBF achieves $O(1)$ membership verification for transaction sets, enabling instantaneous audit and dispute resolution (Jiang et al., 21 Jun 2024). Fraud-proof challenge mechanisms enforce strict economic penalties, stably deterring copy-claim attacks or block-level collusion (Liu et al., 2023).

Performance evaluations indicate that reputation-driven ESP selection achieves roughly 30% reduction in average response latency and effectively balances service workloads, with protocol overheads below 2 seconds per event in edge environments (Liu et al., 2023). On Ethereum testbeds, AIGC-Chain records gas costs ranging from ≈217,370 gas (init) to ≈120,000 gas (per phase), and IBF-based queries yield ~5–10 ms audit times—vastly outperforming linear scan or key-index alternatives (Jiang et al., 21 Jun 2024).

Protocol Phase	Typical Overhead (Gas/time)	Retrieval Complexity
Registration	≈217,370 gas (Eth testbed)	O(1) via IBF
Generation/Upload	≈80,000–120,000 gas	O(1) via IBF
Challenge/Audit	<2 s per event (edge testbed)	O(1) IBF, O(1) sigs

A plausible implication is that the record-indexing approach and enforced cryptoeconomic incentives make these protocols viable for high-throughput, scalable, real-world copyright management.

6. Open Research Directions and Prospective Extensions

Emerging research challenges include blockchain-anchored governance for deepfake detection using on-chain juries or oracles, decentralized training/fine-tuning of AIGC models at the edge with incentive-compatible GPU sharing, smart-contract integration for metaverse 3D asset management, and privacy-preserving zero-knowledge proofs for ownership claims without disclosing underlying data. Key scalability demand points include sharded or layer-2 architectures capable of supporting millions of AIGC registrations and lifecycle events per second (Liu et al., 2023).

Open consortium models (e.g., Hyperledger Fabric) further present vectors for improved auditor neutrality, cross-domain interoperability, and enhanced chaincode extensibility in multi-party copyright adjudication, suggesting a rapidly converging landscape for robust proof-of-AIGC protocol design (Jiang et al., 21 Jun 2024).