DeepSLM: Speckle-Licensed Modulation via Deep Adversarial Learning for Authorized Optical Encryption and Decryption

Abstract

Optical encryption is pivotal in information security, offering parallel processing, speed, and robust security. The simplicity and compatibility of speckle-based cryptosystems have garnered considerable attention. Yet, the predictable statistical distribution of speckle optical fields’ characteristics can invite statistical attacks, undermining these encryption methods. The proposed solution, a deep adversarial learning-based speckle modulation network (DeepSLM), disrupts the strong intercorrelation of speckle grains. Utilizing the unique encoding properties of speckle patterns, DeepSLM facilitates license editing within the modulation phase, pioneering a layered authentication encryption system. Our empirical studies confirm DeepSLM's superior performance on key metrics. Notably, the testing dataset reveals an average Pearson correlation coefficient above 0.97 between decrypted images and their original counterparts for intricate subjects like human faces, attesting to the method's high fidelity. This innovation marries adjustable modification, optical encryption, and deep learning to enforce tiered data access control, charting new paths for creating user-specific access protocols.