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Due to the fact that coverless information hiding can effectively resist the detection of steganalysis tools, it has attracted more attention in the field of information hiding. At present, most coverless information hiding schemes select text and image as transmission carriers, while there are few studies on emerging popular media such as video, which has more abundant contents. Taking the natural video as the carrier is more secure and can avoid the attention of attackers. In this paper, we propose a coverless video steganography algorithm based on semantic segmentation. Specifically, to establish the mapping relationship between secret information and video files effectively, this paper introduces the deep learning based on semantic segmentation network to calculate the statistical histogram of semantic information. To quickly index the sender's secret message to the corresponding video frame, we build a three-digit index structure. The receiver can extract the valid video frame from the three-digit index information and restore the secret information. On the one hand, the neural network is trained through the original image and the noisy image in this scheme; therefore, it can not only effectively resist the interference of noises, but also accurately extract the robust deep features of the image. The frames of video generate the robust mapping to the secret information after the semantic information statistics. On the other hand, semantic segmentation belongs to pixel-level segmentation, which has high requirements for network parameters, so it is difficult for attackers to decrypt and recover secret information. Since this scheme does not modify the primitiveness of video data, it can effectively resist steganalysis tools. The experimental results and analysis show that the video coverless information hiding scheme has a large capacity and a certain resistance to noise attack. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been widely studied on solving intelligent surveillance analysis and security problems in recent years. Although many multimedia security approaches have been proposed by using deep learning network model, there are still some challenges on their performances which deserve in-depth research. On the one hand, high computational complexity of current deep learning methods makes it hard to be applied to real-time scenario. On the other hand, it is difficult to obtain the specific features of a video by fine-tuning the network online with the object state of the first frame, which fails to capture rich appearance variations of the object. To solve above two issues, in this paper, an effective object tracking method with learning attention is proposed to achieve the object localization and reduce the training time in adversarial learning framework. First, a prediction network is designed to track the object in video sequences. The object positions of the first ten frames are employed to fine-tune prediction network, which can fully mine a specific features of an object. Second, the prediction network is integrated into the generative adversarial network framework, which randomly generates masks to capture object appearance variations via adaptively dropout input features. Third, we present a spatial attention mechanism to improve the tracking performance. The proposed network can identify the mask that maintains the most robust features of the objects over a long temporal span. Extensive experiments on two large-scale benchmarks demonstrate that the proposed algorithm performs favorably against state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. Most existing coverless image steganography (CIS) methods achieve excellent robustness under non-geometric attacks. However, they do not perform well under some geometric attacks. Towards this goal, a CIS algorithm based on DenseNet feature mapping is proposed. Deep learning is introduced to extract high-dimensional CNN features which are mapped into hash sequences. For the sender, a binary tree hash index is built to accelerate index speed of searching hidden information and DenseNet hash sequence, and then, all matched images are sent. For the receiver, the secret information can be recovered successfully by calculating the DenseNet hash sequence of the cover image. During the whole steganography process, the cover images remain unchanged. Experimental results and analysis show that the proposed scheme has better robust compared with the state-of-the-art methods under geometric attacks. [ABSTRACT FROM AUTHOR]

In the field of medicine, with the introduction of computer systems that can collect and analyze massive amounts of data, many non-invasive diagnostic methods are being developed for a variety of conditions. In this study, our aim is to develop a non-invasive method of classifying respiratory sounds that are recorded by an electronic stethoscope and the audio recording software that uses various machine learning algorithms. In order to store respiratory sounds on a computer, we developed a cost-effective and easy-to-use electronic stethoscope that can be used with any device. Using this device, we recorded 17,930 lung sounds from 1630 subjects. We employed two types of machine learning algorithms; mel frequency cepstral coefficient (MFCC) features in a support vector machine (SVM) and spectrogram images in the convolutional neural network (CNN). Since using MFCC features with a SVM algorithm is a generally accepted classification method for audio, we utilized its results to benchmark the CNN algorithm. We prepared four data sets for each CNN and SVM algorithm to classify respiratory audio: (1) healthy versus pathological classification; (2) rale, rhonchus, and normal sound classification; (3) singular respiratory sound type classification; and (4) audio type classification with all sound types. Accuracy results of the experiments were; (1) CNN 86%, SVM 86%, (2) CNN 76%, SVM 75%, (3) CNN 80%, SVM 80%, and (4) CNN 62%, SVM 62%, respectively. As a result, we found out that spectrogram image classification with CNN algorithm works as well as the SVM algorithm, and given the large amount of data, CNN and SVM machine learning algorithms can accurately classify and pre-diagnose respiratory audio. [ABSTRACT FROM AUTHOR]