Experimental quantum secret sharing based on phase encoding of coherent states

Quantum secret sharing(QSS)is one of the basic communication primitives in future quantum networks which addresses part of the basic cryptographic tasks of multiparty communication and computation.Nevertheless,it is a challenge to provide a practical QSS protocol with security against general attacks.A QSS protocol that balances security and practicality is still lacking.Here,we propose a QSS protocol with simple phase encoding of coherent states among three parties.Removing the requirement of impractical entangled resources and the need for phase randomization,our protocol can be implemented with accessible technology.We provide the finite-key analysis against coherent attacks and implement a proof-of-principle experiment to demonstrate our scheme’s feasibility.Our scheme achieves a key rate of 85.3 bps under a 35 d B channel loss.Combined with security against general attacks and accessible technology,our protocol is a promising candidate for practical multiparty quantum communication networks.

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.

A Hybrid Security Framework for Medical Image Communication

Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful ownership.Depending on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this attention.This framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using DRPE.Then the encrypted image is watermarking based on WHT.The combination between watermarking and encryption increases the security and robustness of transmitting an image.The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).

Security enhancement for double-random phase encryption using orthogonally encoded image and electronically synthesized key data

We prot）ose a security-enhanced double-random phase encryption （DRPE） scheme using orthogonally encoded image and electronically synthesized key data to cope with the security problem of DRPE technique caused by fixed double-random phase masks for eneryption. In the proposed scheme, we adopt the electronically synthesized key to frequently update the phase mask using a spatial light modulator, and also employ the orthogonal encoding technique to encode the image and electronically synthesized key data, which can enhance the security of both data. We provide detailed procedures for eneryption and decryption of the proposed scheme, and provide the simulation results to show the eneryption effects of the proposed scheme.

Quantitative comparisons between relaxation enhanced compressed sensing 3D MERGE and conventional 3D MERGE for vessel wall imaging in equal scan time: preliminary studies

In this study, we quantitatively compared relaxation enhanced compressed sensing(RECS-3D MERGE) with conventional 3D MERGE techniques on blood suppression efficiency, wall-lumen contrast and plaque burden measurement for carotid atherosclerotic imaging in equal scan time. Twelve patients were recruited in the study. RECS-3D MERGE and conventional 3D MERGE were implemented. 2D DIR-FSE was carried out as a reference standard. The lumen signal-to-tissue ratio(STR) was used as the quantitative measure of blood suppression efficiency. The contrast-to-tissue ratio(CTR) was used as the quantitative measure of wall-lumen contrast. Vessel lumen area(LA) and wall area(WA) were measured for morphological comparisons. The lumen STR of RECS-3D MERGE was significantly lower than that of 3D MERGE while the wall-lumen CTR of RECS-3D MERGE was significantly higher. There were no significant differences in plaque burden measurements between RECS-3D MERGE and 2D DIR-FSE. For comparison between conventional 3D MERGE and 2D DIR-FSE, there were no significant differences in LA measurement. However, the WA of 3D MERGE was significantly larger. The RECS-3D MERGE sequence achieved more sufficient blood suppression and higher image contrast without prolonging the scan time. These improvements lead to more accurate morphological measurements of carotid atherosclerotic imaging.