Untrained neural network for cryptanalysis of a phase-truncated-Fourier-transform-based optical cryptosystem
Shuixin Pan, Meihua Liao, Wenqi He, Yueqiang Zhang, Xiang Peng · Optics Express · 2021
Optical cryptosystem based on phase-truncated-Fourier-transforms (PTFT) is one of the most interesting optical cryptographic schemes due to its unique mechanism of encryption/decryption. Several optical cryptanalysis methods using iterative phase/amplitude retrieval algorithm or deep learning (DL) have also been proposed to analyze the security risks of a PTFT-based cryptosystem. In this work, we proposed an innovative way to attack a PTFT-based cryptosystem with an untrained neural network (UNN) model, where the parameters are optimized with the help of the physical encryption model of a PTFT-based cryptosystem. The proposed method avoids relying on thousands of training data (plaintext-ciphertext pairs), which is an essential but inconvenient burden in the existing data-driven DL-based attack methods. Therefore, the plaintext could be retrieved with good quality from only one ciphertext without any training process. This novel UNN-based attack strategy will open up a new avenue for optical cryptanalysis. Numerical simulations demonstrate the feasibility and effectiveness of the proposed method.