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In recent years, image encryption has been broadly researched. Since a large proportion of images on Internet are compressed, and JPEG is the most widely adopted standards for image compression, a variety of joint image compression and encryption algorithms have been proposed. JPEG image encryption uses signal processing techniques as well as cryptographic techniques in different stages of the JPEG compression process. Based on the location where the encryption is taken place, these algorithms can be generally classified into three major categories: pre‐compression encryption algorithms, in‐compression encryption algorithms, and post‐compression encryption algorithms, which correspond to conduct encryption operations before, during, and after the compression process. From this perspective of classification, the authors give a comprehensive survey on representative image encryption algorithms of each type, and show their properties and limitations. Some of the most recent encryption schemes that achieve protection at various positions of the compression process are selected for comparison, which are two pre‐compression encryption algorithms, four in‐compression encryption algorithms, and two post‐compression encryption algorithms. Possible future research directions on designing joint JPEG compression and encryption schemes are provided in the end, which may facilitate solving application scenario‐oriented JPEG security problems with new technologies. [ABSTRACT FROM AUTHOR]

For joint image compression and encryption schemes, the encryption power and compression efficiency are commonly two contradictory things. In this paper, we propose a new joint image compression and encryption scheme based on a lossy JPEG standard, which aims at encryption power's enhancement, on the premise of maintaining the JPEG's compression efficiency. The proposed scheme is image-content-adaptive, since the secret encryption key is generated from the plain image using the BLAKE2 hash algorithm. Three encryption operations are contained in our scheme, alternating new orthogonal transforms transformation, dc coefficients encryption, and ac coefficients encryption. To save the cost for transmitting different encryption keys each time to decoder for decryption when the plain image changes, we propose embedding the encryption key into the entropy-encoded bitstream of some ac coefficients, and the whole embedding procedure is controlled by another secret key called the embedding key. Extensive experiments are conducted to show that our encryption scheme is JPEG friendly, and has good confusion and diffusion properties. Detailed security analysis is also given to illustrate the proposed scheme's persistence to various cryptanalysis strategies. [ABSTRACT FROM AUTHOR]

With the development of cloud computing and the demand for privacy protection, reversible data hiding in encrypted images (RDHEI), which is a technology that embeds secret data in an encrypted image and can ensure error-free extraction of embedded data and lossless recovery of original images, has received widespread attention in recent years. In this paper, an high-capacity and secure RDHEI method is proposed. First, the pixel value of the original image is predicted. Using the properties of the Laplace distribution, the prediction errors are divided into two types of labels. Adaptive Huffman-encoded labeling is performed on these two labels, respectively. Pixel rearrangement is then used to reserve space where additional data can be embedded. Due to the reversibility of Huffman encoding and decoding, the receiver can achieve separable secret information extraction and lossless recovery of the original image. Experimental results show that compared with several existing methods, our method can achieve higher embedding capacity and good security, not only the invisibility of the image content, but also the undetectability of the secret information. [ABSTRACT FROM AUTHOR]

BackgroundWithout timely diagnosis and treatment, tachycardia, also called tachyarrhythmia, can cause serious complications such as heart failure, cardiac arrest, and even death. The predictive performance of conventional clinical diagnostic procedures needs improvement in order to assist physicians in detecting risk early on. ObjectiveWe aimed to develop a deep tachycardia onset prediction (TOP-Net) model based on deep learning (ie, bidirectional long short-term memory) for early tachycardia diagnosis with easily accessible data. MethodsTOP-Net leverages 2 easily accessible data sources: vital signs, including heart rate, respiratory rate, and blood oxygen saturation (SpO2) acquired continuously by wearable embedded systems, and electronic health records, containing age, gender, admission type, first care unit, and cardiovascular disease history. The model was trained with a large data set from an intensive care unit and then transferred to a real-world scenario in the general ward. In this study, 3 experiments incorporated merging patients’ personal information, temporal memory, and different feature combinations. Six metrics (area under the receiver operating characteristic curve [AUROC], sensitivity, specificity, accuracy, F1 score, and precision) were used to evaluate predictive performance. ResultsTOP-Net outperformed the baseline models on the large critical care data set (AUROC 0.796, 95% CI 0.768-0.824; sensitivity 0.753, 95% CI 0.663-0.793; specificity 0.720, 95% CI 0.645-0.758; accuracy 0.721; F1 score 0.718; precision 0.686) when predicting tachycardia onset 6 hours in advance. When predicting tachycardia onset 2 hours in advance with data acquired from our hospital using the transferred TOP-Net, the 6 metrics were 0.965, 0.955, 0.881, 0.937, 0.793, and 0.680, respectively. The best performance was achieved using comprehensive vital signs (heart rate, respiratory rate, and SpO2) statistical information. ConclusionsTOP-Net is an early tachycardia prediction model that uses 8 types of data from wearable sensors and electronic health records. When validated in clinical scenarios, the model achieved a prediction performance that outperformed baseline models 0 to 6 hours before tachycardia onset in the intensive care unit and 2 hours before tachycardia onset in the general ward. Because of the model’s implementation and use of easily accessible data from wearable sensors, the model can assist physicians with early discovery of patients at risk in general wards and houses.