With the rapid advancement of artificial intelligence, AI-generated content (AIGC) applications are becoming increasingly prevalent. However, it remains challenging to deliver high-quality AIGC services in edge scenarios that have real-time requirements and limited resources. In this paper, we propose BC-MFAIGC, a blockchain-enabled framework for mobile federated AIGC. The proposed BC-MFAIGC architecture coordinates edge computing, federated learning, and blockchain. Edge computing brings selected services closer to terminals, federated learning supports collaborative training without transferring raw user data, and blockchain provides transaction authentication, traceable update records, and governance functions. We evaluate the implemented denoising diffusion probabilistic model (DDPM) federated-training workflow under malicious updates using single-machine simulations. Under a medium-strength Gaussian weight-perturbation condition, the observed Fréchet Inception Distance (FID) increase is approximately 2.5% for BC-MFAIGC-Soft and 9.3% for NOBC-MFAIGC-Soft, compared with about 22% for FedAvg in the three-seed evaluation, while the worker-layer detection rate in the near-threshold regime is 9.03–14.29%, reflecting the difficulty of distinguishing malicious from benign updates when their score distributions overlap. Under the current attack condition, the mean FID across five seeds is 307.62 for BC-MFAIGC-Soft and 285.86 for NOBC-MFAIGC-Soft compared with 372.55 for FedAvg. The paired mean difference between BC-MFAIGC-Soft and FedAvg is $-64.93$ with an unadjusted 95% bootstrap CI ranging from $-96.64$ to $-29.53$. The corresponding difference between NOBC-MFAIGC-Soft and FedAvg is $-86.69$ with an unadjusted 95% bootstrap CI ranging from $-120.75$ to $-50.80$. Both comparisons have Holm-adjusted $p=0.25$. Separate protocol tests cover transaction authentication, block-integrity checking, and replay handling, and the simulated ledger configuration increases round wall-clock time by 8.6%.

To address the issues of low accuracy in transmission line anomaly detection and recognition caused by challenges such as multi-scale targets and partial occlusion, this paper proposes an optimized transmission line anomaly detection algorithm based on improved YOLOv11n. Firstly, an improved Cross Stage Partial with kernel size 2 (C3k2_DFF) module takes the place of the original C3k2 module in the backbone network. It adaptively fuses multi-scale local features and dynamically selects salient channel-wise and spatial features according to its global information during fusion to enhance the model’s feature representation. Secondly, a Separated and Enhancement Attention Module (SEAM) attention mechanism is introduced to enhance the unoccluded area feature response to compensate for the occluded area response deficit, suppressing the background features that interfere with the model and improving the model’s occluded target perception capability. Experimental results on our self-constructed dataset indicate that the proposed improved YOLOv11n model achieves precision, recall, mAP50, and mAP50-95 of 94.2%, 90.8%, 94.3%, and 68.0%, respectively. Compared with the baseline model, it represents improvements of 2.0%, 1.3%, 1.6%, and 2.9%, while the parameters and GFLOPs increase by only 6.6% and 4.8%, demonstrating superior detection performance.