TCA-Oriented Attractor Editing in a Discrete Memristive Rulkov Neuron with Application to Lightweight IoV Image Encryption

The emerging demand for lightweight image security in the Internet of Vehicles (IoV) highlights the need for chaotic sources with programmable dynamical characteristics. This paper develops a three-dimensional discrete memristive Rulkov neuron and proposes a translation-constraint-assignment (TCA) criterion for attractor editing. By partitioning the memristive state space and assigning local gain coefficients to different regions, TCA establishes an explicit mapping among state intervals, local parameters, and attractor properties. Through coordinated translation, regional constraint, and parameter assignment, the proposed criterion enables attractor states to be edited according to prescribed dynamical and spatial requirements within a unified system structure. This mechanism supports the controllable construction of coexisting attractors through regional gain assignments and interval settings, enabling configurable combinations of dynamical states and spatial arrangements. The resulting nonlinear behaviors are analyzed numerically and verified on an STM32 platform. Furthermore, configurable chaotic sequences generated by the edited attractors are combined with SPECK64/128 and bidirectional diffusion to construct a lightweight image encryption scheme. Experimental results demonstrate favorable security, efficiency, and applicability to resource-constrained IoV devices.