Information hiding is an important technique for information security, which is wildly studied by researchers. Recently embedding methods are proposed in spatial, frequency and other domains. After investigating previous literatures, we find that there is still room for embedding performance improvement. Inspired by some literatures, we propose a new method (Modulus Calculations on Prime Number Algorithm, MOPNA) for embedding secret data into cover-images. The main idea of MOPNA is hiding confidential data in paired cover-pixels with modulus calculation based on weight parameters consisting of prime numbers. MOPNA improves the embedding capacity while maintaining good stego-image quality. The correctness of MOPNA method is proved by a combination of mathematical and programming proof. The experimental results prove that the proposed method has high embedding capacity and achieves better comprehensive performance than existing methods.
Confidential information can be hidden in digital images through data hiding technology. This has practical application value for copyright, intellectual property protection, public information protection, and so on. In recent years, researchers have proposed many schemes of data hiding. However, existed data hiding schemes suffer from low hiding capacity or poor stego-image quality. This paper uses a new method of multiple pixels-value adjustment with encoding function (MPA) to further improve the comprehensive performance, which is well in both hiding capacity and stego-image quality. The main idea is to divide n adjacent cover pixels into two sub-groups and implement multi-bit-based modulus operations in each group, respectively. The efficacy of this proposed is evaluated by peak signal-to-noise ratio (PSNR), embedding payload, structural similarity index (SSIM), and quality index (QI). The recorded PSNR value is 30.01 dB, and embedding payload is 5 bpp (bits per pixel). In addition, the steganalysis tests do not detect this steganography technique.
As an infectious disease, pneumonia can cause great harm to human health. If pneumonia can be detected and treated early, its harm will be greatly reduced. Previously, hospitals relied on specialized doctors to diagnose diseases, but with advances in computer technology, deep learning is widely used in the medical field. In recent years, many excellent pneumonia classification methods have been proposed. They can judge whether a patient is infected with pneumonia based on their chest X-ray image, which effectively solves the shortage of professional doctors. In this paper, a convolutional neural network was proposed for pneumonia classification, and the pneumonia classification model was trained based on 1211 real chest X-ray image provided by Third Military Medical University. Experimental results on the test set show that the convolutional neural network proposed in this paper is not dominant, and its classification accuracy is only 72.0%, which is lower than the other three pneumonia classification models compared. Therefore, this paper integrates data enhancement, data preprocessing, transfer learning technology, then proposes a pneumonia classification accuracy enhancement scheme. This scheme improves the classification accuracy of the proposed pneumonia classification model from 72.0% to 82.0%, and the classification effect exceeds the other three pneumonia classification models compared.
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