## 3 Model Assumptions and Construction  
### Model Assumptions  
There are three populations in the copyright protection system: digital content creators; NFT service platforms; and government regulatory authorities. Figure 1 illustrates our system. First, the digital content creators produce works and submit them to the NFT publishing platform for similarity detection. Then, the platform controls the process of minting (Tokenizing) based on the detection results. Finally, the regulator rewards or punishes creators and platforms through feedback from the market. Evolutionary game theory is an effective method for studying the dynamic changes in bounded rational agent strategies in long-term repeated games under information asymmetry conditions [16]. In the game process, each subject integrates various interests, learns, and evolves in the repeated game, and finally makes the optimal decision. Based on the above logic, we propose the following assumptions:  
1. The NFT digital work copyright protection system involves three populations: digital content creators; NFT service platforms; and government regulatory authorities. All players involved exhibit bounded rationality, and their strategic choices tend to converge to stable strategies over time;
2. The strategy space available for digital content creators is \(\alpha=(\alpha_{1},\,\alpha_{2})=(\text{original},\,\text{non-original}/\text{fake})\). In the population of creators, the proportion of entities who adopt the \(\alpha_{1}\) strategy is \(x\), and the proportion of entities who adopt the \(\alpha_{2}\) strategy is \((1-x)\); \(x\in[0,1]\). The strategy space of NFT digital publishing platforms is \(\beta=(\beta_{1},\,\beta_{2})=(\text{fair detection},\,\text{collusive detection})\). In the population of platforms, the proportion of entities who adopt the \(\beta_{1}\) strategy is \(y\), and the proportion of entities who adopt the \(\beta_{2}\) strategy is \((1-y)\); \(y\in[0,\,1]\). The strategy selection space of regulators is \(\gamma=(\gamma_{1},\,\gamma_{2})=(\text{strict supervision, loose supervision})\). In the population of regulators, the proportion of entities who adopt the \(\gamma_{1}\) strategy is \(z\), and the proportion of entities who adopt the \(\gamma_{2}\) strategy is \((1-z)\); \(z\in[0,1]\);3. The sales revenue of NFT is \(R_{\text{c}}\). The cost of designing and producing original digital works is \(C_{\text{co}}\). The cost of producing non-original digital works is \(C_{\text{cf}}\), \(C_{\text{co}}>C_{\text{cf}}\). Original digital content can definitely pass similarity detection. The non-original digital content can only pass similarity detection when the NFT service platform conducts collusion detection, and the cost of collusion is \(C_{\text{cfr}}\), \(C_{\text{cfr}}<(C_{\text{co}}-C_{\text{cf}})\). Meanwhile, the speculative act of producing non-original works will incur speculative costs \(C_{\text{cfs}}\), mainly including expenses on information search, false advertising, and others. The production activities of creators are aimed at economic benefits and require initial parameters to meet \(R_{\text{c}}>C_{\text{co}}>C_{\text{cf}}+C_{\text{cfr}}+C_{\text{cfs}}\);
4. Digital content works can only be sold on the blockchain after successfully passing similarity detection, and the detection revenue is \(R_{\text{P}}\). When digital content is non-original, if the service platform conducts fair detection, it is not qualified. If the platform conducts collusion detection, it reaches collusion with the infringer and helps the infringing work obtain the online license. The speculative cost of conducting collusion detection for the platform is \(C_{\text{P}}\), including expenses on modifying detection reports, strengthening information security management, and others. The activities of platforms are driven by economic benefits and require initial parameters to meet \(C_{\text{cfr}}>C_{\text{P}}\);
5.