T-TNet: A dual-dependency trigger framework for active defense and hierarchical access control via multi-domain information fusion

As deep neural networks (DNNs) are increasingly deployed in multi-sensor fusion systems operating under low-security conditions, they are exposed to serious threats such as model theft, parameter tampering, and unauthorized usage. To address these challenges, this paper proposes an active defense framework based on personalized frequency-domain triggers—T-TNet (TPS-Transform Triggered Defense Network). T-TNet fuses diverse feature information with a user-defined private point set through a dedicated fusion mechanism, enabling fine-grained behavior regulation and secure access control. On authorized data, T-TNet generates normal predictions; on unauthorized data, it outputs misleading predictions, thereby significantly enhancing model security. Experimental results demonstrate that, compared to baseline accuracy, T-TNet’s performance drops by no more than 2% while limiting the prediction accuracy on unauthorized data to below 5%. Moreover, T-TNet improves overall predictive performance by 24.69% compared to the latest research. This innovative framework offers a proactive defense strategy for protecting the intellectual property of deep learning models, particularly in low-security multi-sensor environments.