A True Random Number Generator Based on Multimodal Feature Fusion from Video

Random numbers play a particularly critical role in cryptographic applications, especially true random numbers (TRNs). However, generating TRNs typically incurs high costs. This paper proposes a novel True Random Number Generator (TRNG) method based on multimodal feature fusion from smartphone video. The proposed approach innovatively leverages video frames, audio signals, and simulated triaxial accelerometer data as heterogeneous entropy sources. By applying grayscale conversion and generalized cat map scrambling to image frames, followed by vectorization, and combining with normalized audio waveforms and perturbed inertial sensor data, a high-entropy composite source is constructed. A post-processing stage then employs a Chaotic Hash function based on a piecewise linear chaotic map (PWLCM) to produce highly unpredictable and cryptographically strong random bit sequences. The generated sequences are rigorously evaluated using the NIST SP 800-22 statistical test suite. Experimental results demonstrate that the proposed method outperforms traditional TRNG schemes based solely on image entropy in both randomness quality and computational efficiency. Furthermore, the system operates without requiring external hardware, indicating strong practical viability and scalability.