Deceptive evidence detection of belief functions based on reinforcement learning in partial label environment

Counter-deception evidence fusion is a critical issue in the application of Dempster–Shafer Theory (DST). Effectively detecting deceptive evidence poses a significant challenge in DST-based information fusion. Existing research on this topic is limited and often lacks a clear distinction between deceptive and credible evidence. Recently, two explicit definitions of deceptive evidence have been proposed to address different scenarios: one for cases with label information and another for cases without. However, these definitions are somewhat counter-intuitive and do not address situations where partial label information is available.

To address this gap, our paper introduces a new, explicit definition of deceptive evidence that considers both the characteristics of the evidence and the fusion system. This definition encompasses cases including with label information, without label information, and with partial label information. It extends the two previously mentioned definitions and, in certain circumstances, aligns with them.

Based on our new definition, we propose a mathematical model for counter-deception evidence fusion across these three scenarios and apply reinforcement learning to solve it. We present several numerical simulations, a data-driven counter-deception test, and a practical application to demonstrate that our method outperforms previous approaches in both detecting deceptive evidence and in practical applications, showcasing superior effectiveness and robustness.