Deep-learning-based cross-talk free and high-security compressive encryption with spatially incoherent illumination

Incoherent optical cryptosystem is promising for its immunity against coherent noise and insensitivity to misalignment, and compressive encryption is desirable considering the increasingly demand on the exchange of encrypted data via Internet. In this paper, we propose a novel optical compressive encryption approach with spatially incoherent illumination based on deep learning (DL) and space multiplexing. For encryption, the plaintexts are individually sent to the scattering-imaging-based encryption (SIBE) scheme where they are transformed to scattering images with noise appearances. Afterwards, these images are randomly sampled and then integrated into a single package (i.e., ciphertext) by space multiplexing. The decryption is basically the inverse of the encryption, while it involves an ill-posed problem (i.e., recovering the noise-like scattering image from its randomly sampled version). We demonstrated that such a problem can be well resolved by DL. The proposal is radically free from the cross-talk noise existing in many current multiple-image encryption schemes. Also, it gets rid of the linearity bothering the SIBE and is hence robust against the ciphertext-only attack based on phase retrieval algorithm. We present a series of experimental results to confirm the effectiveness and feasibility of the proposal. (c) 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

In this paper, a novel optical compressive encryption approach based on deep learning and space multiplexing is proposed. The plaintexts are transformed into scattering images with noise appearances using scattering-imaging-based encryption scheme, and then randomly sampled and integrated into a ciphertext by space multiplexing. The decryption process involves an ill-posed problem, which can be well resolved by deep learning. Experimental results confirm the effectiveness and feasibility of the proposal.