Processing In-Memory PUF Watermark Embedding With Cellular Memristor Network
Alex Pappachen James, Chithra Reghuvaran, Leon Ong Chua · IEEE Transactions on Emerging Topics in Computing · 2025
The cellular neural network (CNN or CeNN) is known to be useful because of its suitability in real-time processing, parallel processing, robustness, flexibility, and energy efficiency. CeNNs have a large number of interconnected processing elements, which can be programmed to produce a wide range of patterns, including regular and irregular patterns, random patterns, and more. When implemented in memristive hardware, the pattern generator ability and inherent variability of memristive devices can be explored to create Physical Unclonable Functions (PUFs). This work reports a method of using memristive CeNNs to perform image processing tasks along with PUF image generation. The CeNN-PUF has dual mode capability combining data processing and encryption using PUF image watermarking. The proposed method provides unique device-specific image watermarks, following a two-stage process of (1) device-specific secret mask generation and (2) watermark embedding. The system is evaluated using multiple CeNN cloning templates and the robustness of the method is validated against ML attacks. A detailed analysis is presented to evaluate the uniqueness, randomness and reliability against different environmental changes. The experimental validation of the proposed model is done on FPGA Xilinx Zynq-7010 processor and benchmarked the system against quantization noise.