Robust Reversible Audio Watermarking Scheme for Telemedicine and Privacy Protection

The leakage of medical audio data in telemedicine seriously violates the privacy of patients.In order to avoid the leakage of patient information in telemedicine,a two-stage reversible robust audio watermarking algorithm is proposed to protect medical audio data.The scheme decomposes the medical audio into two independent embedding domains,embeds the robust watermark and the reversible watermark into the two domains respectively.In order to ensure the audio quality,the Hurst exponent is used to find a suitable position for watermark embedding.Due to the independence of the two embedding domains,the embedding of the second-stage reversible watermark will not affect the first-stage watermark,so the robustness of the first-stage watermark can be well maintained.In the second stage,the correlation between the sampling points in the medical audio is used to modify the hidden bits of the histogram to reduce the modification of the medical audio and reduce the distortion caused by reversible embedding.Simulation experiments show that this scheme has strong robustness against signal processing operations such as MP3 compression of 48 db,additive white Gaussian noise(AWGN)of 20 db,low-pass filtering,resampling,re-quantization and other attacks,and has good imperceptibility.

A Robust 3-D Medical Watermarking Based on Wavelet Transform for Data Protection

In a telemedicine diagnosis system,the emergence of 3D imaging enables doctors to make clearer judgments,and its accuracy also directly affects doctors’diagnosis of the disease.In order to ensure the safe transmission and storage of medical data,a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper.The proposed algorithm employs the principal component analysis(PCA)transform to reduce the data dimension,which can minimize the error between the extracted components and the original data in the mean square sense.Especially,this algorithm helps to create a bacterial foraging model based on particle swarm optimization(BF-PSO),by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding,thereby achieving the optimal balance between embedding capacity and imperceptibility.A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.

Coverless Information Hiding Based on the Molecular Structure Images of Material

The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.

A Fusion Steganographic Algorithm Based on Faster R-CNN

The aim of information hiding is to embed the secret message in a normal cover media such as image,video,voice or text,and then the secret message is transmitted through the transmission of the cover media.The secret message should not be damaged on the process of the cover media.In order to ensure the invisibility of secret message,complex texture objects should be chosen for embedding information.In this paper,an approach which corresponds multiple steganographic algorithms to complex texture objects was presented for hiding secret message.Firstly,complex texture regions are selected based on a kind of objects detection algorithm.Secondly,three different steganographic methods were used to hide secret message into the selected block region.Experimental results show that the approach enhances the security and robustness.

QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut(Arachis hypogaea L.)

To dissect the genetic mechanism of multi-seed pod in peanut, we explored the QTL/gene controlling multi-seed pod and analyzed the interaction effect of QTL and environment. Two hundred and forty eight recombinant inbred lines(RIL) from cross Silihong × Jinonghei 3 were used as experimental materials planted in 8 environments from 2012 to 2017. Three methods of analysis were performed. These included individual environment analysis, joint analysis in multiple environments, and epistatic interaction analysis for multi-seed pod QTL. Phenotypic data and best linear unbiased prediction(BLUP) value of the ratio of multi-seed pods per plant(RMSP) were used for QTL mapping. Seven QTL detected by the individual environmental mapping analysis and were distributed on linkage groups 1, 6, 9, 14, 19(2), and 21. Each QTL explained 4.42%–11.51% of the phenotypic variation in multi-seed pod, and synergistic alleles of5 QTL were from the Silihong parent. One QTL, explaining 4.93% of the phenotypic variation was detected using BLUP data, and this QTL mapped in the same interval as q RMSP19.1 detected in the individual environment analysis. Seventeen additive QTL were identified by joint analysis across multiple environments. A total of 43 epistatic QTL were detected by ICIM-EPI mapping in the multiple environment trials(MET) module, and involved 57 loci. Two main-effect QTL related to multi-seed pod in peanut were filtered. We also found that RMSP had a highly significant positive correlation with pod yield per plant(PY), and epistatic effects were much more important than additive effects. These results provide theoretical guidance for the genetic improvement of germplasm resources and further fine mapping of related genes in peanut.

A Mobile Localization Strategy for Wireless Sensor Network in NLOS Conditions

The problem of mobile localization for wireless sensor network has attracted considerable attention in recent years. The localization accuracy will drastically grade in non-line of sight(NLOS) conditions. In this paper, we propose a mobile localization strategy based on Kalman filter. The key technologies for the proposed method are the NLOS identification and mitigation. The proposed method does not need the prior knowledge of the NLOS error and it is independent of the physical measurement ways. Simulation results show that the proposed method owns the higher localization accuracy when compared with other methods.

GNP6,a novel allele of MOC1,regulates panicle and tiller development in rice

The yield of rice is mostly affected by three factors,namely,panicle number,grain number and grain weight.Variation in panicle and grain numbers is mainly caused by tiller and panicle branches generated from axillary meristems(AMs).MOC1 encodes a putative GRAS family nuclear protein that regulates AM formation.Although several alleles of MOC1 have been identified,its variation in germplasm resources remains unclear.In the present study we characterized a novel mocl allele named gnp6 which has a thymine insertion in the coding sequence of the SAW motif in the GRAS domain.This mutation causes arrested branch formation.The SAW motif is necessary for nuclear localization of GNP6/MOC1 where it functions as a transcription factor or co-regulator.Haplotype analysis showed that the coding region of GNP6/MOC1 was conserved without any non-synonymous mutations in 240 rice accessions.However,variation in the promoter region might affect the expression of it and its downstream genes.Joint haplotype analysis of GNP6/MOC1 and MOC3 showed that haplotype combinations H9,H10 and H11,namely MOC1-Hap1 in combination with MOC3-Hap3,MOC3-Hap4 or MOC3-Hap5 could be bred to promote branch formation.These findings will enrich the genetic resources available for rice breeders.

Fingerprint Liveness Detection from Different Fingerprint Materials Using Convolutional Neural Network and Principal Component Analysis

Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints,which are made of common fingerprint materials,such as silicon,latex,etc.Thus,to protect our privacy,many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint.Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features,but these methods normally destroy or lose spatial information between pixels.Different from existing methods,convolutional neural network(CNN)can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled data.Thus,CNN is explored to solve the above problem and discriminate true fingerprints from fake ones in this paper.To reduce the redundant information and extract the most distinct features,ROI and PCA operations are performed for learned features of convolutional layer or pooling layer.After that,the extracted features are fed into SVM classifier.Experimental results based on the LivDet(2013)and the LivDet(2011)datasets,which are captured by using different fingerprint materials,indicate that the classification performance of our proposed method is both efficient and convenient compared with the other previous methods.

Efficient Algorithm for K-Barrier Coverage Based on Integer Linear Programming

Barrier coverage of wireless sensor networks is an important issue in the detection of intruders who are attempting to cross a region of interest.However,in certain applications,barrier coverage cannot be satisfied after random deployment.In this paper,we study how mobile sensors can be efficiently relocated to achieve k-barrier coverage.In particular,two problems are studied:relocation of sensors with minimum number of mobile sensors and formation of k-barrier coverage with minimum energy cost.These two problems were formulated as 0–1 integer linear programming(ILP).The formulation is computationally intractable because of integrality and complicated constraints.Therefore,we relax the integrality and complicated constraints of the formulation and construct a special model known as RELAX-RSMN with a totally unimodular constraint coefficient matrix to solve the relaxed 0–1 ILP rapidly through linear programming.Theoretical analysis and simulation were performed to verify the effectiveness of our approach.

Fingerprint Liveness Detection Based on Multi-Scale LPQ and PCA

Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.

A Novel Steganography Scheme Combining Coverless Information Hiding and Steganography

At present,the coverless information hiding has been developed.However,due to the limited mapping relationship between secret information and feature selection,it is challenging to further enhance the hiding capacity of coverless information hiding.At the same time,the steganography algorithm based on object detection only hides secret information in foreground objects,which contribute to the steganography capacity is reduced.Since object recognition contains multiple objects and location,secret information can be mapped to object categories,the relationship of location and so on.Therefore,this paper proposes a new steganography algorithm based on object detection and relationship mapping,which integrates coverless information hiding and steganography.In this method,the coverless information hiding is realized by mapping the object type,color and secret information in object detection method.At the same time,the object detection method is used to find the safe area to hide secret messages.The proposed algorithm can not only improve the steganographic capacity of the two information hiding methods but also make the coverless information hiding more secure and robust.

Intelligent Mobile Drone System Based on Real-Time Object Detection

Drone also known as unmanned aerial vehicle(UAV)has drawn lots of attention in recent years.Quadcopter as one of the most popular drones has great potential in both industrial and academic fields.Quadcopter drones are capable of taking off vertically and flying towards any direction.Traditional researches of drones mainly focus on their mechanical structures and movement control.The aircraft movement is usually controlled by a remote controller manually or the trajectory is pre-programmed with specific algorithms.Consumer drones typically use mobile device together with remote controllers to realize flight control and video transmission.Implementing different functions on mobile devices can result in different behaviors of drones indirectly.With the development of deep learning in computer vision field,commercial drones equipped with camera can be much more intelligent and even realize autonomous flight.In the past,running deep learning based algorithms on mobile devices is highly computational intensive and time consuming.This paper utilizes a novel real-time object detection method and deploys the deep learning model on the modern mobile device to realize autonomous object detection and object tracking of drones.

A Novel Steganography Algorithm Based on Instance Segmentation

Information hiding tends to hide secret information in image area where is rich texture or high frequency,so as to transmit secret information to the recipient without affecting the visual quality of the image and arousing suspicion.We take advantage of the complexity of the object texture and consider that under certain circumstances,the object texture is more complex than the background of the image,so the foreground object is more suitable for steganography than the background.On the basis of instance segmentation,such as Mask R-CNN,the proposed method hides secret information into each object's region by using the masks of instance segmentation,thus realizing the information hiding of the foreground object without background.This method not only makes it more efficient for the receiver to extract information,but also proves to be more secure and robust by experiments.

A Dual Model Watermarking Framework for Copyright Protection in Image Processing Networks

Image processing networks have gained great success in many fields,and thus the issue of copyright protection for image processing networks hasbecome a focus of attention. Model watermarking techniques are widely usedin model copyright protection, but there are two challenges: (1) designinguniversal trigger sample watermarking for different network models is stilla challenge;(2) existing methods of copyright protection based on trigger swatermarking are difficult to resist forgery attacks. In this work, we propose adual model watermarking framework for copyright protection in image processingnetworks. The trigger sample watermark is embedded in the trainingprocess of the model, which can effectively verify the model copyright. And wedesign a common method for generating trigger sample watermarks based ongenerative adversarial networks, adaptively generating trigger sample watermarksaccording to different models. The spatial watermark is embedded intothe model output. When an attacker steals model copyright using a forgedtrigger sample watermark, which can be correctly extracted to distinguishbetween the piratical and the protected model. The experiments show that theproposed framework has good performance in different image segmentationnetworks of UNET, UNET++, and FCN (fully convolutional network), andeffectively resists forgery attacks.

High Visual Quality Image Steganography Based on Encoder-Decoder Model

Nowadays,with the popularization of network technology,more and more people are concerned about the problem of cyber security.Steganography,a technique dedicated to protecting peoples’private data,has become a hot topic in the research field.However,there are still some problems in the current research.For example,the visual quality of dense images generated by some steganographic algorithms is not good enough;the security of the steganographic algorithm is not high enough,which makes it easy to be attacked by others.In this paper,we propose a novel high visual quality image steganographic neural network based on encoder-decoder model to solve these problems mentioned above.Firstly,we design a novel encoder module by applying the structure of U-Net++,which aims to achieve higher visual quality.Then,the steganalyzer is heuristically added into the model in order to improve the security.Finally,the network model is used to generate the stego images via adversarial training.Experimental results demonstrate that our proposed scheme can achieve better performance in terms of visual quality and security.

A Food Traceability Framework Based on Permissioned Blockchain

In recent years,food safety problems have become increasingly serious.The traditional supply chain traceability solution faces some serious problems,such as centralization,data tampering,and high communication costs.To solve these problems,this paper proposes a food traceability framework based on permissioned blockchain.The proposed framework is decentralized,and the supply chain data of the framework cannot be tampered with.The framework divides supply chain entities into five organizations,and each organization deploys its own chaincode onto the blockchain.The chaincode specifies the query permission of each organization,which can effectively protect the user’s sensitive information.The PBFT consensus algorithm adopted by the framework improves the performance of processing transactions.The transactional throughput experiment shows that the proposed framework can achieve a high number of transactions per second.Query efficiency experiment demonstrates that the framework has lower query latency and good user experience.

A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

Fingerprint identification systems have been widely deployed in many occasions of our daily life.However,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit fingerprints.To address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s attention.The vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model generalization.Aiming at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint detection.Next,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection model.Finally,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and cross-sensor.The size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

Robust Watermarking of Screen-Photography Based on JND

With the popularity of smartphones,it is often easy to maliciously leak important information by taking pictures of the phone.Robust watermarking that can resist screen photography can achieve the protection of information.Since the screen photo process can cause some irreversible distortion,the currently available screen photo watermarks do not consider the image content well and the visual quality is not very high.Therefore,this paper proposes a new screen-photography robust watermark.In terms of embedding region selection,the intensity-based Scale-invariant feature transform(SIFT)algorithm used for the construction of feature regions based on the density of feature points,which can make it more focused on the key content of the image;in terms of embedding strength,the Just noticeable difference(JND)model is applied to limit the intensity of the watermark embedding according to the luminance and texture of the picture to balance robustness and invisibility;after embedding watermark,the coefficients in the neighborhood are again adjusted with optimal constraints to improve the accuracy of watermark extraction.After experiments,it is shown that the method we proposed can improve the correct rate of watermark extraction,the quality of the visual aspect of the watermarked picture is also improved.

A Derivative Matrix-Based Covert Communication Method in Blockchain

The data in the blockchain cannot be tampered with and the users are anonymous,which enables the blockchain to be a natural carrier for covert communication.However,the existing methods of covert communication in blockchain suffer from the predefined channel structure,the capacity of a single transaction is not high,and the fixed transaction behaviors will lower the concealment of the communication channel.Therefore,this paper proposes a derivation matrix-based covert communication method in blockchain.It uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full use of the redundancy of addresses.Subsequently,to solve the problem of the lack of concealment caused by the fixed transaction behaviors,divide the rectangular matrix into square blocks with overlapping regions and then encrypt different blocks sequentially to make the transaction behaviors of the channel addresses match better with those of the real addresses.Further,the linear congruence algorithm is used to generate random sequence,which provides a random order for blocks encryption,and thus enhances the security of the encryption algorithm.Experimental results show that this method can effectively reduce the abnormal transaction behaviors of addresses while ensuring the channel transmission efficiency.

AMDnet:An Academic Misconduct Detection Method for Authors’Behaviors

In recent years,academic misconduct has been frequently exposed by the media,with serious impacts on the academic community.Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript.For the most part,these techniques can only detect cases of textual plagiarism,which means that potential culprits can easily avoid discovery through clever editing and alterations of text content.In this paper,we propose an academic misconduct detection method based on scholars’submission behaviors.The model can effectively capture the atypical behavioral approach and operation of the author.As such,it is able to detect various types of misconduct,thereby improving the accuracy of detection when combined with a text content analysis.The model learns by forming a dual network group that processes text features and user behavior features to detect potential academic misconduct.First,the effect of scholars’behavioral features on the model are considered and analyzed.Second,the Synthetic Minority Oversampling Technique(SMOTE)is applied to address the problem of imbalanced samples of positive and negative classes among contributing scholars.Finally,the text features of the papers are combined with the scholars’behavioral data to improve recognition precision.Experimental results on the imbalanced dataset demonstrate that our model has a highly satisfactory performance in terms of accuracy and recall.