With the widespread deployment of deep learning models in multi-party collaborative scenarios, the issues of secure model access control and intellectual property (IP) protection have become increasingly critical. To address the limitations of existing methods that lack proactive defense mechanisms in such settings, this paper introduces a novel paradigm Consensus Learning which enables fine-grained control over model execution permissions via a multi-party joint authorization mechanism. Building on this, we propose the Collaborative Perturbation Trigger Method (CPTM), which allows participating parties to collaboratively generate perturbation-based trigger data that embed identity features. The model can only be activated using the collectively constructed trigger, enforcing tightly bound access control without modifying the model architecture. Extensive experiments on CIFAR-10, CIFAR-100, MNIST, and Face-LFW datasets demonstrate that the proposed method maintains prediction accuracy within 2% of the baseline unprotected models on authorized data. In contrast, under unauthorized or adversarial inputs, model accuracy drops below 10%, showcasing strong access control capabilities and robustness. This study offers a novel direction for building scalable, robust, and proactively protected deep learning models in multi-party collaborative environments.