Deep-learning-based ciphertext-only attack on optical double random phase encryption

Optical cryptanalysis is essential to the further investigation of more secure optical cryptosystems.Learning-based at-tack of optical encryption eliminates the need for the retrieval of random phase keys of optical encryption systems but it is limited for practical applications since it requires a large set of plaintext-ciphertext pairs for the cryptosystem to be at-tacked.Here,we propose a two-step deep learning strategy for ciphertext-only attack(COA)on the classical double ran-dom phase encryption(DRPE).Specifically,we construct a virtual DRPE system to gather the training data.Besides,we divide the inverse problem in COA into two more specific inverse problems and employ two deep neural networks(DNNs)to respectively learn the removal of speckle noise in the autocorrelation domain and the de-correlation operation to retrieve the plaintext image.With these two trained DNNs at hand,we show that the plaintext can be predicted in real-time from an unknown ciphertext alone.The proposed learning-based COA method dispenses with not only the retrieval of random phase keys but also the invasive data acquisition of plaintext-ciphertext pairs in the DPRE system.Numerical simulations and optical experiments demonstrate the feasibility and effectiveness of the proposed learning-based COA method.

Identity Authentication Based on Two-Beam Interference

A two-factor identity authentication method on the basis of two-beam interference was presented. While verifying a user’s identity, a specific “phase key” as well as a corresponding “phase lock” are both mandatory required for a successful authentication. Note that this scheme can not only check the legality of the users, but also verify their identity levels so as to grant them hierarchical access permissions to various resources of the protected systems or organizations. The authentication process is straightforward and could be implemented by a hybrid optic-electrical system. However, the system designing procedure involves an iterative Modified Phase Retrieval Algorithm (MPRA) and can only be achieved by digital means. Theoretical analysis and simulations both validate the effectiveness of our method.

CD97 negatively regulates the innate immune response against RNA viruses by promoting RNF125-mediated RIG-I degradation

The G protein-coupled receptor ADGRE5(CD97)binds to various metabolites that play crucial regulatory roles in metabolism.However,its function in the antiviral innate immune response remains to be determined.In this study,we report that CD97 inhibits virus-induced type-I interferon(IFN-I)release and enhances RNA virus replication in cells and mice.CD97 was identified as a new negative regulator of the innate immune receptor RIG-I,and RIG-1 degradation led to the suppression of the IFN-I signaling pathway.Furthermore,overexpression of CD97 promoted the ubiquitination of RIG-I,resulting in its degradation,but did not impact its mRNA expression.Mechanistically,CD97 upregulates RNF125 expression to induce RNF125-mediated RIG-I degradation via K48-linked ubiquitination at Lys181 after RNA virus infection.Most importantly,CD97-deficient mice are more resistant than wild-type mice to RNA virus infection.We also found that sanguinarine-mediated inhibition of CD97 effectively blocks VSV and SARS-CoV-2 replication.These findings elucidate a previously unknown mechanism through which CD97 negatively regulates RIG-I in the antiviral innate immune response and provide a molecular basis for the development of new therapeutic strategies and the design of targeted antiviral agents.