Encrypt a Story: A Video Segment Encryption Method Based on the Discrete Sinusoidal Memristive Rulkov Neuron

Traditional video encryption methods protect video content by encrypting each frame individually. However, in resource-constrained environments, this approach consumes significant computational resources. To overcome this challenge, this paper proposes a novel method called “Encrypt a story (EAS)”, which aims to enhance encryption efficiency by focusing on encrypting specific segments of the video rather than encrypting each frame. The EAS refers to selecting segments in the time dimension of the video that contain important information or key events for encryption. This method leverages video segmentation techniques to focus encryption efforts on continuous key frames, significantly reducing the consumption of computational resources. To address the need for a large number of key streams during the encryption process, this paper proposes a discrete sinusoidal memristive Rulkov neuron map (DSM-RNM). Through attractor analysis, complexity comparison, Lyapunov exponent, and NIST tests, we validated its ability to generate high-performance pseudorandom sequences, which significantly enhances the security of the encryption algorithm. Notably, the DSM-RNM is shown to exhibit a phenomenon of infinitely coexisting attractors. Furthermore, by constructing a digital circuit to capture the attractors of the DSM-RNM, its potential for industrial applications is demonstrated. Evaluation results show that the EAS saves approximately 90% of the time while ensuring security, exhibiting strong practicality and efficiency