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Watermark Embedding for Direct Binary Searched Halftone Images by Adopting Visual Cryptography
Get PDFIn this paper, two methods are proposed to embed visual watermark into direct binary search (DBS) halftone images, which are called Adjusted Direct Binary Search (ADBS) and Dual Adjusted Direct Binary Search (DADBS). DADBS is an improved version of ADBS. By using the proposed methods, the visual watermark will be embedded into two halftone images separately, thus, the watermark can be revealed when these two halftone images are overlaid. Experimental results show that both methods can achieve excellent image visual quality and decoded visual patterns
Robust Image Hashing via Random Gabor Filtering and DWT
Get PDFImage hashing is a useful multimedia technology for many applications, such as image authentication, image retrieval, image copy detection and image forensics. In this paper, we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform (DWT). Specifically, robust and secure image features are first extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map, and then are compressed via a single-level 2-D DWT. Image hash is finally obtained by concatenating DWT coefficients in the LL sub-band. Many experiments with open image datasets are carried out and the results illustrate that our hashing is robust, discriminative and secure. Receiver operating characteristic (ROC) curve comparisons show that our hashing is better than some popular image hashing algorithms in classification performance between robustness and discrimination
Feature Selection Method Based on Class Discriminative Degree for Intelligent Medical Diagnosis
Get PDFBy using efficient and timely medical diagnostic decision making, clinicians can positively impact the quality and cost of medical care. However, the high similarity of clinical manifestations between diseases and the limitation of clinicians’ knowledge both bring much difficulty to decision making in diagnosis. Therefore, building a decision support system that can assist medical staff in diagnosing and treating diseases has lately received growing attentions in the medical domain. In this paper, we employ a multi-label classification framework to classify the Chinese electronic medical records to establish corresponding relation between the medical records and disease categories, and compare this method with the traditional medical expert system to verify the performance. To select the best subset of patient features, we propose a feature selection method based on the composition and distribution of symptoms in electronic medical records and compare it with the traditional feature selection methods such as chi-square test. We evaluate the feature selection methods and diagnostic models from two aspects, false negative rate (FNR) and accuracy. Extensive experiments have conducted on a real-world Chinese electronic medical record database. The evaluation results demonstrate that our proposed feature selection method can improve the accuracy and reduce the FNR compare to the traditional feature selection methods, and the multi-label classification framework have better accuracy and lower FNR than the traditional expert system
A Novel Universal Steganalysis Algorithm Based on the IQM and the SRM
Get PDFThe state-of-the-art universal steganalysis method, spatial rich model (SRM), and the steganalysis method using image quality metrics (IQM) are both based on image residuals, while they use 34671 and 10 features respectively. This paper proposes a novel steganalysis scheme that combines their advantages in two ways. First, filters used in the IQM are designed according to the models of the SRM owning to their strong abilities for detecting the content adaptive steganographic methods. In addition, a total variant (TV) filter is also used due to its good performance of preserving image edge properties during filtering. Second, due to each type of these filters having own advantages, the multiple filters are used simultaneously and the features extracted from their outputs are combined together. The whole steganalysis procedure is removing steganographic noise using those filters, then measuring the distances between images and their filtered version with the image quality metrics, and last feeding these metrics as features to build a steganalyzer using either an ensemble classifier or a support vector machine. The scheme can work in two modes, the single filter mode using 9 features, and the multi-filter mode using 639 features. We compared the performance of the proposed method, the SRM and the maxSRMd2. The maxSRMd2 is the improved version of the SRM. The simulated results show that the proposed method that worked in the multi-filter mode was about 10% more accurate than the SRM and maxSRMd2 when the data were globally normalized, and had similar performance with the SRM and maxSRMd2 when the data were locally normalized
An Empirical Comparison on Multi-Target Regression Learning
Get PDFMulti-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It has received relatively small attention from the Machine Learning community. However, multi-target regression exists in many real-world applications. In this paper we conduct extensive experiments to investigate the performance of three representative multi-target regression learning algorithms (i.e. Multi-Target Stacking (MTS), Random Linear Target Combination (RLTC), and Multi-Objective Random Forest (MORF)), comparing the baseline single-target learning. Our experimental results show that all three multi-target regression learning algorithms do improve the performance of the single-target learning. Among them, MTS performs the best, followed by RLTC, followed by MORF. However, the single-target learning sometimes still performs very well, even the best. This analysis sheds the light on multi-target regression learning and indicates that the single-target learning is a competitive baseline for multi-target regression learning on multi-target domains
Multi-VMs Intrusion Detection for Cloud Security Using Dempster-shafer Theory
Get PDFCloud computing provides easy and on-demand access to computing resources in a configurable pool. The flexibility of the cloud environment attracts more and more network services to be deployed on the cloud using groups of virtual machines (VMs), instead of being restricted on a single physical server. When more and more network services are deployed on the cloud, the detection of the intrusion likes Distributed Denial-of-Service (DDoS) attack becomes much more challenging than that on the traditional servers because even a single network service now is possibly provided by groups of VMs across the cloud system. In this paper, we propose a cloud-based intrusion detection system (IDS) which inspects the features of data flow between neighboring VMs, analyzes the probability of being attacked on each pair of VMs and then regards it as independent evidence using Dempster-Shafer theory, and eventually combines the evidence among all pairs of VMs using the method of evidence fusion. Unlike the traditional IDS that focus on analyzing the entire network service externally, our proposed algorithm makes full use of the internal interactions between VMs, and the experiment proved that it can provide more accurate results than the traditional algorithm
Phishing Detection with Image Retrieval Based on Improved Texton Correlation Descriptor
Get PDFAnti-detection is becoming as an emerging challenge for anti-phishing. This paper solves the threats of anti-detection from the threshold setting condition. Enough webpages are considered to complicate threshold setting condition when the threshold is settled. According to the common visual behavior which is easily attracted by the salient region of webpages, image retrieval methods based on texton correlation descriptor (TCD) are improved to obtain enough webpages which have similarity in the salient region for the images of webpages. There are two steps for improving TCD which has advantage of recognizing the salient region of images: (1) This paper proposed Weighted Euclidean Distance based on neighborhood location (NLW-Euclidean distance) and double cross windows, and combine them to solve the problems in TCD; (2) Space structure is introduced to map the image set to Euclid space so that similarity relation among images can be used to complicate threshold setting conditions. Experimental results show that the proposed method can improve the effectiveness of anti-phishing and make the system more stable, and significantly reduce the possibilities of being hacked to be used as mining systems for blockchain
Analysis and Improvement of an Efficient Controlled Quantum Secure Direct Communication and Authentication Protocol
Get PDFThe controlled quantum secure direct communication (CQSDC) with authentication protocol based on four particle cluster states via quantum one-time pad and local unitary operations is cryptanalyzed. It is found that there are some serious security issues in this protocol. An eavesdropper (Eve) can eavesdrop on some information of the identity strings of the receiver and the controller without being detected by the selective-CNOT-operation (SCNO) attack. By the same attack, Eve can also steal some information of the secret message that the sender transmits. In addition, the receiver can take the same kind of attack to eavesdrop on some information of the secret message out of the control of the controller. This means that the requirements of CQSDC are not satisfied. At last, we improve the original CQSDC protocol to a secure one
Numerical Visualizations of Mixing Enhancement in a 2D Supersonic Ejector
Get PDFThe present study deals with the numerical visualization of the mixing process in a 2D supersonic ejector. The mixing process is visualized using two CFD flow visualization methods. The first method consists in introducing discrete particles in the secondary flow and computing their trajectories. The second method consists in modeling the diffusion of a passive scalar introduced in one of the two flows. The mixing process is investigated in the case of a conventional 2D supersonic ejector and a second case of an ejector equipped with transverse micro jets. Flow visualizations obtained show the existence of a significant mixing enhancement when the ejector mixing chamber is equipped with wall slot injectors
Distance Control Algorithm for Automobile Automatic Obstacle Avoidance and Cruise System
Get PDFWith the improvement of automobile ownership in recent years, the incidence of traffic accidents constantly increases and requirements on the security of automobiles become increasingly higher. As science and technology develops constantly, the development of automobile automatic obstacle avoidance and cruise system accelerates gradually, and the requirement on distance control becomes stricter. Automobile automatic obstacle avoidance and cruise system can determine the conditions of automobiles and roads using sensing technology, automatically adopt measures to control automobile after discovering road safety hazards, thus to reduce the incidence of traffic accidents. To prevent accidental collision of automobile which are installed with automatic obstacle avoidance and cruise system, active brake should be controlled during driving. This study put forward a neural network based proportional-integral-derivative (PID) control algorithm. The active brake of automobiles was effectively controlled using the system to keep the distance between automobiles. Moreover the algorithm was tested using professional automobile simulation platform. The results demonstrated that neural network based PID control algorithm can precisely and efficiently control the distance between two cars. This work provides a reference for the development of automobile automatic obstacle avoidance and cruise system