In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.

Perceptual Generative Adversarial Networks for Small Object Detection

Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to super-resolved ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K [45] and the Caltech [9] benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.

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With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability.

/pdf/inertial-sensor-based-gait-recognition-a-review-4k8qgcb7y2.pdf

Inertial Sensor-Based Gait Recognition: A Review

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Handbook Of Biometrics

Authentication systems using gait captured from inertial sensors have been recently developed to enhance the limitation of existing mechanisms on mobile devices and achieved promising results. However, most these systems employed pattern recognition and machine learning techniques in which biometric templates are stored insecurely, which could leave critical security and user privacy issues. Specifically, a compromise of original gait templates could result in everlasting forfeiture. In this paper, two main results will be presented. Firstly, we propose a novel gait authentication system on mobile devices in which the security and privacy are preserved by employing a fuzzy commitment scheme. Instead of storing original gait templates for user verification like in conventional approaches, we verify the user via a stored key which is biometrically encrypted by gait templates collected from a mobile accelerometer. Secondly, the discriminability of sensor-based gait templates are investigated to determine appropriate parameter values to construct an effective gait-based biometric cryptosystem. The performance of our proposed system is evaluated on the dataset including gait signals of 34 volunteers. We achieved the zeroFAR and the False Rejection Rate of approximately 16.18 % corresponding to the key length, as well as the system security level of 139 bits. The results from our experiment show that accelerometer-based gait could be further investigated to construct a biometric cryptosystem, as effective as other biometric traits such as iris, fingerprint, voice, and signature.

Gait authentication on mobile phone using biometric cryptosystem and fuzzy commitment scheme

This paper presents a new approach for color detection and segmentation based on Support Vector Machine (SVM) to retrieve candidate regions of traffic signs in real-time video processing. Instead of processing on each pixel, this approach utilizes a block of pixels as an input vector of SVM for color classification, where the dimension of each vector can be extended by a group of neighboring pixels. This helps to handle the diversification of data on both training and testing samples. After that, Hough transform and contour detection are applied to verify the candidate regions by detecting shapes of circle and triangle. The experimental results are highly accurate and robust for our testing database, where samples are recorded on various states of environment.

Real time traffic sign detection using color and shape-based features

Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer

Inertial Measurement Units (IMUs)-based gait analysis is a promising and attractive approach for user recognition Recently, the adoption of deep learning techniques has gained significant performance improvement However, most existing studies focused on exploiting the spatial information of gait data (using Convolutional Neural Network (CNN)) while the temporal part received little attention In this study, we propose a new multi-model Long Short-term Memory (LSTM) network for learning the gait temporal features First , we observe that LSTM is able to capture the pattern hidden inside the gait data sequences that are out-of-synchronization Thus, instead of using the gait cycle-based segment, our model accepts the gait cycle-free segment ( ie, fixed-length window) as the input By this, the classification task does not depend on the gait cycle detection task, which usually suffers from noise and bias Second , we propose a new LSTM network architecture, in which, one LSTM is used for each gait data channel and a group of consecutive signals is processed in each step This strategy allows the network to effectively handle the long input data sequence and achieve improved performance compared to existing LSTM-based gait models In addition, besides using the LSTM alone, we extend it by combining with a CNN model to construct a hybrid network, which further improves the recognition performance We evaluated our LSTM and hybrid networks under different settings using the whuGAIT and OU-ISIR datasets The experiments showed that our LSTM network outperformed the existing LSTM networks, and its combination with CNN established new state-of-the-art performance on both the verification and identification tasks

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Multi-Model Long Short-Term Memory Network for Gait Recognition Using Window-Based Data Segment

Blockchain technology offers great benefits to the development of information technology. However, in order to use blockchain effectively, it is necessary to consider security and privacy aspects. This paper presents a survey of different security and privacy aspects of blockchain. In particular, we present the common types of security attacks on blockchain and security enhancement solutions. We also focus on the existing solutions to protect privacy for blockchain, and on the directions for future research using group signature and zero-knowledge schemes. Group signature scheme can be used to validate transactions in a blockchain network, and zero-knowledge to guarantee that transactions are valid despite the fact that the detailed information of transactions remains obscure. The combination of group signature and zero-knowledge could be feasible solutions for improving security and privacy for blockchain. This solution could also be deployed for IoT networks.

Thuc D. Nguyen

Papers

Gait authentication on mobile phone using biometric cryptosystem and fuzzy commitment scheme

Real time traffic sign detection using color and shape-based features

Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer

Multi-Model Long Short-Term Memory Network for Gait Recognition Using Window-Based Data Segment

A Survey on Security and Privacy Issues of Blockchain Technology