Motivation

Table of Links

Abstract and 1. Introduction

1.1 Background

1.2 Motivation

1.3 Our Work and Contributions and 1.4 Organization

2. Related Work

   2.1 Mobile AIGC and Its QoE Modeling

   2.2 Blockchain for Mobile Networks

3. Preliminaries

4. Prosecutor Design

   4.1 Architecture Overview

   4.2 Reputation Roll-up

   4.3 Duplex Transfer Channel

5. OS2a: Objective Service Assessment for Mobile AIGC

   5.1 Inspiration from DCM

   5.2 Objective Quality of the Service Process

   5.3 Subjective Experience of AIGC Outputs

6. OS2A on Prosecutor: Two-Phase Interaction for Mobile AIGC

   6.1 MASP Selection by Reputation

   6.2 Contract Theoretic Payment Scheme

7. Implementation and Evaluation

   7.1 Implementation and Experimental Setup

   7.2 Prosecutor Performance Evaluation

   7.3 Investigation of Functional Goals

   7.4 Security Analysis

8. Conclusion and References

1.2 Motivation

Although similar mechanisms have been studied separately in many other scenarios, the unique features of mobile AIGC bring brand-new challenges. Firstly, in traditional service markets, such as edge offloading, effective service provider selection can be realized by firstly modeling the Quality of Experience (QoE) from the client perspective and then selecting the service providers leading to the highest QoE [7], [8]. However, such schemes fail to support the emerging AIGC scenario due to the following reasons.

• Multimodality: AIGC is going beyond multimedia content generation and aiming to provide an immersive fusion of multimodal services [6]. However, most quantitative metrics for QoE measurement are modalityspecific [9], [10], [11]. Hence, we need to extend various QoE models that adapt to different AIGC modalities, which are inflexible and cannot support the evercomplicated mobile AIGC applications.

• Subjectivity: AIGC outputs can be regarded as novel digital artwork whose judgment suffers from intrinsic subjectivity. Different clients may evaluate an AIGC output from different aspects. For instance, even if an AIGC image performs well in the PyTorch Image Quality tests [12], it may not achieve satisfying QoE if its style (e.g., realism or abstractism) does not match the client’s expectations and personal preference.

For the payment scheme, the clients suffer from information asymmetry in mobile AIGC [13]. To be specific, since the resources invested by MASPs for performing AIGC inferences are unobserved, the clients are threatened by the moral hazard [14]. In this case, if the clients pay the fixed AIGC service fee in one lump sum, dishonest MASPs might not provide high-quality service as promised to save computation resources. Finally, the fee-ownership transfers in mobile AIGC are vulnerable since the anonymous clients and MASPs may repudiate without being afraid of prosecution. For example, clients can cancel ongoing payments immediately after receiving the AIGC output and vice versa. Consequently, the atomicity, i.e., whether the operations in one transfer all occur or nothing occurs, is broken.