Abstract

We present a practical solution for generating 360◦stereo panoramic videos using a single camera. Current approaches either use a moving camera that captures multiple images of a scene, which are then stitched together to form the final panorama, or use multiple cameras that are synchronized. A moving camera limits the solution to static scenes, while multi-camera solutions require dedicated calibrated setups. Our approach improves upon the existing solutions in two significant ways: It solves the problem using a single camera, thus minimizing the calibration problem and providing us the ability to convert any digital camera into a panoramic stereo capture device. It captures all the light rays required for stereo panoramas in a single frame using a compact custom designed mirror, thus making the design practical to manufacture and easier to use. We analyze several properties of the design as well as present panoramic stereo and depth estimation results.

Abstract

Fine-grained classification is an extremely challenging problem in computer vision, compounded by subtle differences in shape, pose, illumination and appearance. While convolutional neural networks have become the versatile jack-of-all-trades tool in modern computer vision, approaches for fine-grained recognition still rely on localization of keypoints and parts to learn discriminative features for recognition. In order to achieve this, most approaches use a localization module and subsequently learn classifiers for the inferred locations,thus necessitating large amounts of manual annotations for bounding boxes and key points. In order to tackle this problem,we aim to leverage the (taxonomic and/or semantic) relationships present among fine-grained classes. The ontology tree is a free source of labels that can be used as auxiliary tasks to train a multi-task loss. Additional tasks can act as regularizers, and increase the generalization capabilities of the network. Multiple tasks try to take the network in diverging directions, and the network has to reach a common minimum by adapting and learning features common to all tasks in its shared layers.We train a multi-task network using auxiliary tasks extracted from taxonomical or semantic hierarchies, using a novel method to update task-wise learning rates to ensure that the related tasks aid and unrelated tasks does not hamper performance on the primary task. Experiments on the popular CUB-200-2011 dataset show that employing super-classes in an end-to-end model improves performance, compared to methods employing additional expensive annotations such as key points and bounding boxes and/or using multi-stage pipelines.

Abstract

In computer vision, many active illumination techniques em-ploy Projector-Camera systems to extract useful information from the scenes. Known illumination patterns are projected onto the scene and their deformations in the captured images are then analyzed. We observe that the local frequencies in the captured pattern for the mirror-like surfaces is different from the projected pattern. This property allows us to design a custom Projector-Camera system to segment mirror-like surfaces by analyzing the local frequencies in the captured images. The system projects a sinusoidal pattern and capture the images from projector’s point of view. We present segmentation results for the scenes including multiple reflections and inter-reflections from the mirror-like sur-faces. The method can further be used in the separation of direct and global components for the mirror-like surfaces by illuminating the non-mirror-like objects separately. We show how our method is also useful for accurate estimation of shape of the non-mirror-like regions in the presence of mirror-like regions in a scene.

Abstract

The widespread popularity of the computer networks has triggered concerns about information security. Password-based user authentication with key agreement protocols have drawn attentions since it provides proper authentication of a user before granting access right to services, and then ensure secure communication over insecure channels. Recently, Lee et al. pointed out different security flaws on Tsaur et al.’s multi-server user authentication protocol, and they further proposed an extended chaotic maps-based user authentication with key agreement protocol for multi-server environments. However, we observed that Lee et al.’s protocol has some functionality and security flaws, i.e., it is inefficient in detection of unauthorized login and it does not support password change mechanism. Besides, their protocol is vulnerable to registration center spoofing attack and server spoofing attack. In order to remedy the aforementioned flaws, we proposed a novel chaotic maps-based user authentication with key agreement protocol for multi-server environments. The proposed protocol is provably secure in the random oracle model under the chaotic-maps based computational Diffie-Hellman assumption. In addition, we analyzed our protocol using BAN logic model. We also compared our protocol with Lee et al.’s protocol in aspects of computation cost, functionalities and securities.

Abstract

Authentication protocol plays an important role in the short-range wireless communications for the Near Field Communication (NFC) technology. Due to the shared nature of wireless communication networks, there are several kinds of security vulnerabilities. Recently, a pseudonym-based NFC protocol (PBNFCP) has been proposed to withstand the security pitfalls found in the existing conditional privacy preserving security protocol (CPPNFC). However, this paper further analyzes PBNFCP and shows that it still fails to prevent the claimed security properties, such as impersonation attacks against an adversary, who is a malicious registered user having a valid pseudonym and corresponding private key. In order to overcome these security drawbacks, this paper proposes a secure and efficient authentication protocol (SEAP) for NFC applications using lifetime-based pseudonyms. The proposed SEAP is simulated for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool. The simulation results show that SEAP is secure. The rigorous security and performance analysis shows that the proposed SEAP is secure and efficient as compared to the related existing authentication protocols for NFC applications.

Abstract

A wireless medical sensor network (WMSN) is a professional application of the traditional wireless body area sensor networks in medicine. Using WMSNs, the parameters of patients’ vital signs can be gathered from the sensor nodes deployed on the body of the patients and accessed by the healthcare professionals by using a mobile device. Due to wireless communication, securing communication becomes a vital issue in WMSNs. Since the vital signs parameters are sensitive to the patients’ health status and these information must not be revealed to the others except the healthcare professionals, the protection of patients’ privacy becomes another key issue for WMSNs applications. Thus, user authentication with anonymity property is the most basic and commonly used method in order to resolve the security and privacy issues of WMSNs. He et al. presented a user authentication protocol for healthcare applications using WMSNs to protect the security and privacy problems. However, Li et al. showed that their scheme is incorrect in authentication and session key agreement phase, has no wrong password detection mechanism and is vulnerable to denial of service caused by password change with wrong password. In this paper, we review Li et al.’s scheme and show that their scheme is still vulnerable to privileged-insider attack, sensor node capture attack and fails to provide user anonymity property. Moreover, we find that He et al.’s scheme is still vulnerable to the same attacks as we find out in Li et al.’s scheme. In order to remedy the security weak-nesses found in both He et al.’s scheme and Li et al.’s scheme, we present a secure biometrics-based user authentication scheme in WMSNs using smart card. Through the rigorous formal and informal security analysis, we show that our scheme is secure against possible known attacks. In addition, we simulate our scheme using the most-widely accepted and used Automated Validation of Internet Security Protocols and Application stool and the simulation results reveal that our scheme is secure. Our scheme is also efficient in computation and communication as compared to He et al.’s scheme, Li et al.’sscheme and other related schemes.

Abstract

Rapid development of the wireless communication technology and low cost of sensing devices has accelerated the development of wireless sensor networks (WSNs). These types of networks have a wide range of applications including habitat monitoring, health monitoring, data acquisition in hazardous environmental conditions and military operations. Sensor nodes are resource constrained having limited communication range, battery and processing power. Sensor nodes are prone to failure and can be also physically captured by an adversary. One of the main concerns in WSNs is to provide security, especially in the cases where they are deployed for military applications and monitoring. Further, WSNs are prone to various attacks such as wormhole, sinkhole and blackhole attacks. A blackhole attack is a kind of denial of service attack, which is very difficult to detect and defend and such blackhole attack, if happens, affects the entire performance of the network. In addition, it causes high end-to-end delay and less throughput with less packet delivery ratio. The situation can be worst if multiple blackhole attacker nodes present in the network. As a result, detection and prevention of the blackhole attack becomes crucial in WSNs. In this paper, we aim to propose a new efficient group-based technique for the detection and prevention of multiple blackhole attacker nodes in WSNs. In our approach, the entire WSN is divided into several clusters and each cluster has a powerful high-end sensor node (called a cluster head), which is responsible for the detection of blackhole attacker nodes, if present, in that cluster. The proposed scheme achieves about 90 % detection rate and 3.75 % false positive rate, which are significantly better than the existing related schemes. Furthermore, our scheme is efficient and thus, it is very appropriate for practical applications in WSNs.

Abstract

Due to the rapid development of wireless communication systems, authentication becomes a key security component in smart grid environments. Authentication then plays an important role in the smart grid domain by providing a variety of security services including credentials' privacy, session-key (SK) security, and secure mutual authentication. In this paper, we analyze the security of a recent relevant work in smart grid, and it is unfortunately not able to deal with SK-security and smart meter secret credentials' privacy under the widely accepted Canetti-Krawczyk adversary (CK-adversary) model. We then propose a new efficient provably secure authenticated key agreement scheme for smart grid. Through the rigorous formal security analysis, we show that the proposed scheme achieves the well-known security functionalities including smart meter credentials' privacy and SK-security under the CK-adversary model. The proposed scheme reduces the computation overheads for both smart meters and service providers. Furthermore, the proposed scheme offers more security functionalities as compared to the existing related schemes.

Abstract

Sensor nodes in a wireless sensor network (WSN) may be lost due to enervation or malicious attacks by an adversary. WSNs deployed for several applications including military applications are prone to various attacks, which degrade the network performance very rapidly. Hybrid anomaly is a type of anomaly that contains the different types of attacker nodes such as blackhole, misdirection, wormhole etc. These multiple attacks can be launched in the network using the hybrid anomaly. In this situation, it is very difficult to find out which kind of attacker nodes are activated in the network. This motivates us to design a robust and efficient secure intrusion detection approach in order to extend thelifetime of a WSN. In this paper, we aim to propose a new intrusion detection technique forhybrid anomaly, which uses the existing data mining algorithm, called K-means clustering.For the detection purpose, patterns of intrusions are built automatically by the K-mean sclustering algorithm over training data. After that intrusions are detected by matching network activities against these detection patterns. We evaluate our approach over a WSNd ataset that is created using Opnet modeler, which contains various attributes, such as end-to-end delay, traffic sent and traffic received. The training data set contains the normal values of the network parameters. The testing dataset is created in actual working mode consists of normal and abnormal values of the network parameters. The proposed tech-nique has the ability to detect two types of malicious nodes: black hole and misdirection nodes. Our scheme achieves 98.6 % detection rate and 1.2 % false positive rate, which are significantly better than the existing related schemes.

Abstract

In recent years, high utility itemsets (HUIs) mining from the transactional databases becomes one of the most emerging research topic in the field of data mining due to its wide range of applications in online e-commerce data analysis, identifying interesting patterns in biomedical data and for cross marketing solutions in retail business. It aims to discover the itemsets with high utilities efficiently by considering item quantities in a transaction and profit values of each item. However, it produces a tremendous number of HUIs, which imposes further burden in analysis of the extracted patterns and also degrades the performance of mining methods. Mining the set of closed + high utility itemsets (CHUIs) solves this issue as it is a loss-less and condensed representation of all HUIs. In this paper, we aim to present a new algorithm for finding CHUIs from a transactional database, called the CHUM (Closed + High Utility itemset Miner), which is scalable and efficient. The proposed mining algorithm adopts a tricky aimed vertical representation of the database in order to speed up the execution time in generating itemset closures and compute their utility information without accessing the database. The proposed method makes use of the item co-occurrences strategy in order to further reduce the number of intersections needed to be performed. Several experiments are conducted on various sparse and dense datasets and the simulation results clearly show the scalability and superior performance of our algorithm as compared to those for the existing state-of-the-art CHUD (Closed + High Utility itemset Discovery) algorithm.