A Nano-Design of Image Masking and Steganography Structure Based on Quantum Technology

Abstract

Secure image storage and transmission require sound encryption methods that resist key exposure while maintaining high image quality. Various encryption approaches have been developed to protect image content and its transmission from unauthorized access. One such method is image masking, where a special mask is generated to conceal information within the original image. Instead of hiding the image visually, the mask creates an intermediate layer that obfuscates the encryption key, eliminating the need to transmit it directly. However, implementing such masking techniques efficiently at the hardware level poses particular challenges. Traditional Complementary Metal-Oxide-Semiconductor (CMOS)-based Very-Large-Scale-Integration (VLSI) systems face scalability issues, excessive heat, and high-power consumption. To overcome these challenges, this study utilizes a nano-scale image masking architecture based on Quantum-dot Cellular Automata (QCA), offering reduced area, lower power dissipation, and faster processing. The core operations utilize a three-input XOR gate, designed as a single-layer QCA structure without rotated cells. While QCA-based approaches improve hardware efficiency, most existing implementations focus only on grayscale images, leaving a gap in colorful image encryption. To address this, the work presents a QCA-based encryption and masking architecture for colored images. The method encrypts an image using a random key to generate a cipher image, which is then XORed with the original image to produce a mask. This process, applied independently to each RGB channel, produces three cipher-mask pairs, embedding steganographic property by concealing key information within the image. The keys are generated using a true random number generator (TRNG) based on cross-coupled loops and crossoriented structures, ensuring high entropy. The design was modeled in QCADesigner 2.0.3, with the encryption/decryption algorithms implemented in Python. Experimental results demonstrated a meaningful reduction in cell count and consumed area compared to the prior designs. Image quality and security analysis confirmed visual fidelity and improved robustness.