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Object detection based on pre-trained deep neural networks (DNNs) has achieved impressive performance and enabled many applications. However, DNN-based object detectors are shown to be vulnerable to physical adversarial attacks. Despite that recent efforts have been made to defend against these attacks, they either use strong assumptions or become less effective with pre-trained object detectors. In this paper, we propose adversarial pixel masking (APM), a defense against physical attacks, which is designed specifically for pre-trained object detectors. APM does not require any assumptions beyond the "patch-like" nature of a physical attack and can work with different pre-trained object detectors of different architectures and weights, making it a practical solution in many applications. We conduct extensive experiments, and the empirical results show that APM can significantly improve model robustness without significantly degrading clean performance.

This paper proposes a novel audio CAPTCHA system that requires a user to respond immediately after hearing a short and easy-to-remember cue in its mixture with background music. Potential attacking paths based on cross correlation (CC) and sound event detection (SED) are implemented to test the security of the system. Then, two defending measures based on phase-modification of the cue and audio watermarking are established against CC and SED-based attacks, respectively. Human subjects were recruited to test the system and the results indicated that the subjects can pass the proposed audio CAPTCHA >90% of the times. In contrast, the proposed defending measures suppress the passing rate of both attacks to 8.2% and 0.4%, respectively.

Recent deterministic database systems have achieved high scalability and high availability in distributed environments given OLTP workloads. However, modern OLTP applications usually have changing workloads or access patterns, so how to make the resource provisioning elastic to the changing workloads becomes an important design goal for a deterministic database system. Live migration, which moves the specified data from a source machine to a destination node while continuously serving the incoming transactions, is a key technique required for the elasticity. In this paper, we present MgCrab, a live migration technique for a deterministic database system, that leverages the determinism to maintain the consistency of data on the source and destination nodes at very low cost during a migration period. We implement MgCrab on an open-source database system. Extensive experiments were conducted and the results demonstrate the effectiveness of MgCrab.

With the growing popularity of Social Networking Services (SNSs), increasing amounts of sensitive information are stored online and linked to SNS accounts. The obvious value of SNS accounts gives rise to the identity fraud problem—unauthorized, stealthy use of SNS accounts. For example, anxious parents may use their children’s SNS accounts to spy on the children’s social interaction; or husbands/wives may check their spouses’ SNS accounts if they suspect infidelity. Stealthy identity fraud could happen to anyone and seriously invade the privacy of account owners. However, there is no known defense against such behavior when an attacker, possibly an acquaintance of the victim, gets access to the victim’s computing devices. In this paper, we propose to extend the use of continuous authentication to detect the in situ identity fraud incidents , which occurs when the attackers use the same accounts, the same devices, and IP addresses as the victims. Using Facebook as a case study, we show that it is possible to detect such incidents by analyzing SNS users’ browsing behavior. Our experiment results demonstrate that the approach can achieve higher than 80% detection accuracy within 2 min, and over 90% after 7 min of observation time.

Cognitive radios (CRs) are proposed to alleviate the huge need for radio spectrum. There are two known steps for a pair of CRs to start communication: the rendezvous and data-channel negotiation . Despite that the rendezvous can be achieved by some well-studied techniques such as the channel hopping, the strategies for data-channel negotiation receive much less attention and their impact on data transmission performance remains unclear. In this paper, we study existing data-channel negotiation schemes for channel-hopping CRs and observe that 1) for short data transmission, they incur a huge overhead, called notification delay , that severely limits the throughput; and 2) for long data transmission, they lead to large overhead, called interruption delay , in handling the PU interruption, which makes performance unstable. By carefully re-examining the steps toward low-overhead and stable data transmission, we argue that a key step, called self-channel selection , is missing and should precede the rendezvous and data channel selection steps. In this step, each CR selects, in a distributed manner, only a small amount of the most stable channels to be used in the later steps. To realize the self-channel selection, we introduce a Randomly-Started Stability-Descent (RSSD) selection algorithm. Expensive simulations are conducted and the results demonstrate the effectiveness of RSSD in reducing 1) the notification delay; 2) the chance of PU interruption; and 3) the interruption delay if PU interruption occurs, which overall improve the performance and quality of data transmission.

We study the problem of region-semantics preserving (RSP) image synthesis. Given a reference image and a region specification R, our goal is to train a model that is able to generate realistic and diverse images, each preserving the same semantics as that of the reference image within the region R. This problem is challenging because the model needs to (1) understand and preserve the marginal semantics of the reference region; i.e., the semantics excluding that of any subregion; and (2) maintain the compatibility of any synthesized region with the marginal semantics of the reference region. In this paper, we propose a novel model, called the fast region-semantics preserver (Fast-RSPer), for the RSP image synthesis problem. The Fast-RSPer uses a pre-trained GAN generator and a pre-trained deep feature extractor to generate images without undergoing a dedicated training phase. This makes it particularly useful for the interactive applications. We conduct extensive experiments using the real-world datasets and the results show that Fast-PSPer can synthesize realistic, diverse RSP images efficiently.

Asynchronous wakeup schemes have been proposed for ad hoc networks to increase the energy efficiency of wireless communication. The basic idea is to allow a node to sleep when it is idle, and wakeup periodically to check if there are pending transmissions. In this paper, we examine the applicability of asynchronous wakeup schemes to the Mobile Ad Hoc NETworks (MANETs). We discover that, although it is desirable to have nodes with lower mobility to sleep more in reaction to the less-changing link states, in practice this is prohibited due to an unwanted tradeoff between the energy saving and in-time link discovery. All nodes in a network must stay awake frequently based on their highest possible moving speed to avoid network partition. To address this problem, we propose a new wakeup scheme, named Unilateral- (Uni-) scheme, for MANETs that allows nodes with slower moving speed to sleep more without losing the network connectivity. The Uni-scheme supports both the entity mobility and group mobility of nodes, thus has broad applicability. Theoretical analysis and simulation are conducted and show that the Uni-scheme can render significant energy saving as compared with the previous arts.

The Support Vector Machines (SVMs) have been widely used for classification due to its ability to give low generalization error. In many practical applications of classification, however, the wrong prediction of a certain class is much severer than that of the other classes, making the original SVM unsatisfactory. In this paper, we propose the notion of Asymmetric Support Vector Machine (ASVM), an asymmetric extension of the SVM, for these applications. Different from the existing SVM extensions such as thresholding and parameter tuning, ASVM employs a new objective that models the imbalance between the costs of false predictions from different classes in a novel way such that user tolerance on false-positive rate can be explicitly specified. Such a new objective formulation allows us of obtaining a lower false-positive rate without much degradation of the prediction accuracy or increase in training time. Furthermore, we show that the generalization ability is preserved with the new objective. We also study the effects of the parameters in ASVM objective and address some implementation issues related to the Sequential Minimal Optimization (SMO) to cope with large-scale data. An extensive simulation is conducted and shows that ASVM is able to yield either noticeable improvement in performance or reduction in training time as compared to the previous arts.

Deterministic database systems have been shown to yield high throughput on a cluster of commodity machines while ensuring the strong consistency between replicas, provided that the data can be well-partitioned on these machines. However, data partitioning can be suboptimal for many reasons in real-world applications. In this paper, we present T-Part, a transaction execution engine that partitions transactions in a deterministic database system to deal with the unforeseeable workloads or workloads whose data are hard to partition. By modeling the dependency between transactions as a T-graph and continuously partitioning that graph, T-Part allows each transaction to know which later transactions on other machines will read its writes so that it can push forward the writes to those later transactions immediately after committing. This forward-pushing reduces the chance that the later transactions stall due to the unavailability of remote data. We implement a prototype for T-Part. Extensive experiments are conducted and the results demonstrate the effectiveness of T-Part.

Clustering in wireless ad hoc networks has shown to be a promising technique to ensure the scalability and efficiency of various communication protocols. Since stations in these networks are usually equipped with batteries as the power source, it is critical to ensure the energy efficiency of a clustering scheme. The Quorum-based Power Saving (QPS) protocols are widely studied over the past years, as they render extensive energy conservation comparing to the IEEE 802.11 Power Saving (PS) mode. However, most existing QPS protocols adopt a symmetric design where each pair of stations in a network are guaranteed to discover each other. Observing that in clustered environments there is no need to insist on all-pair neighbor discovery, we propose an Asymmetric Cyclic Quorum (ACQ) system. The ACQ system guarantees the neighbor discovery between each member node and the clusterhead in a cluster, and between clusterheads in the network. We show that by taxing slightly more energy consumption on the clusterhead, the average energy consumption of stations in a cluster can reduce substantially than can be achieved by traditional QPS protocols. A novel construction scheme is proposed in this work, which assembles the ACQ system in O(1) time. The constructing scheme is adaptive. Stations in a cluster can adjust their awake/sleep ratio collaboratively to strike the balance between energy efficiency and delay under various cluster conditions. Simulation results show that the ACQ system outperforms the previous studies up to 52% in energy efficiency, while introducing no extra worst-case latency.

Intelligent Transportation Systems (ITS) improve passenger/pedestrian safety and transportation productivity through the use of vehicle-to-vehicle and vehicle-to-roadside wireless communication technologies. Communication protocols in these environments must meet strict delay requirements due to the high moving speed of the vehicles. In this paper, we propose an energy-conservative MAC layer protocol, named DSRC-AA, based on IEEE 802.11 that provides power saving to the ITS communication modules (e.g., On Board Units, portable devices, and Road Side Units) while ensuring the bounded delay. DSRC-AA, a generalization of the Asynchronous Quorum-based Power-Saving (AQPS) protocols, capitalizes on the clustering nature of moving vehicles and assigns different wake-up/sleep schedules to the clusterhead and the members of a cluster. DSRC-AA is able to dynamically adapt the schedules to meet the communication delay requirements at various vehicle moving speed. Simulation results show that DSRC-AA is able to yield more than 44 percent reduction in average energy consumption as compared with the existing AQPS protocols, if to be used in vehicular networks.

As networking technology advances, more advanced message services are provided. Users may have one or more different message accounts and devices. Before sending messages, the sender must make sure which message service the receipt currently uses. Any misjudgement may delay the time when the messages are received. To make users be able to receive messages anytime and anywhere with any kind of devices, we propose a Ubiquitous and Unified Multimedia Messaging (UMM) platform. The UMM platform integrates different message services and provides a more efficient way for message delivery. Our design does not modify the existing protocols of message services and need not involve the network operators. An analytical model is proposed to evaluate the performance of the implemented platform. Our study shows that with a large number of message services the user subscribes and long message processing time in the network, the delayed message probability can be limited within 1.5%. This performance is considered satisfactory.

In this paper, we propose to use a continuous authentication approach to detect the in-situ identity fraud incidents, which occur when the attackers use the same devices and IP addresses as the victims. Using Facebook as a case study, we show that it is possible to detect such incidents by analyzing SNS users' browsing behavior. Our experiment results demonstrate that the approach can achieve reasonable accuracy given a few minutes of observation time.

Cognitive radio (CR) is intended to meet the expo- nentially growing demand for spectrum by allowing for oppor- tunistic utilization of idle legacy channels. Rendezvous ,w here two radios complete handshaking in an idle channel, is a key step for "stranger" (unknown to each other) CRs to start communication. However, none of existing algorithms guarantee rendezvous for heterogeneous or stranger CRs with different spectrum-sensing capabilities, in spite of the fact that (i) a wide variety of mobile devices are equipped with heterogeneous radios and (ii) there are numerous applications requiring efficient rendezvous for heterogeneous radios/CRs. In this paper, we propose a new channel hopping algorithm, called Heterogeneous Hopping (HH), that guarantees rendezvous without assuming existence of a universal channel set that can be sensed by all radios. HH is realized with a two-layer design that harmonizes the fixed-short-cycle and parity-alignment techniques we propose here, in order to guide CRs to rendezvous in two com- plementary situations resulting from the different capabilities of mobile wireless devices. To best of our knowledge, HH is the first channel-hopping scheme that guarantees rendezvous between heterogeneous radios. Our in-depth evaluation has shown HH to be significantly faster than simple extensions of existing schemes. Moreover, the latter cannot guarantee successful rendezvous, either.

Cognitive radio allows radio devices to access the idle spectrum opportunistically, thus alleviates the huge demand for spectrum. Rendezvous, where two radios complete handshaking in an idle channel, is a key step for cognitive radios to start communication. Radios may have the same (homogeneous) or different (heterogeneous) spectrum sensing capabilities. Currently, there is a "gap" between the rendezvous algorithms for homogeneous and heterogeneous cognitive radios---existing homogeneous algorithms incur high delay when applied to heterogeneous radios; while heterogeneous algorithms incur high congestion when applied to homogeneous radios. Since mixtures of these two types of radios appear commonly in practice, it is crucial to bridge the gap between the respective rendezvous algorithms. In this paper, we propose a new rendezvous algorithm, named the ICH scheme, for arbitrary mixtures of radios with homogeneous or heterogeneous spectrum sensing capabilities. Rigorous analysis and extensive simulations are conducted and show that ICH is the first rendezvous scheme that guarantees rendezvous for arbitrary mixtures of homogeneous and heterogeneous radios without incurring large delay and congestion.

Asynchronous wakeup schemes have been proposed for ad hoc networks to increase the energy efficiency of wireless communication. The basic idea is to allow a node to sleep when it is idle, and wakeup periodically to check if there are pending transmissions. In this paper we examine the applicability of asynchronous wakeup schemes to the Mobile Ad Hoc Networks (MANETs). We discover that, although it is desirable to have nodes with lower mobility to sleep more in reaction to the less-changing link states, in practice this is prohibited due to an unwanted tradeoff between the energy saving and in-time link discovery. All nodes in a network must stay awake frequently based on their highest possible moving speed in order to avoid network partition. To address this problem, we propose a new wakeup scheme, named Unilateral- (Uni-) scheme, for MANETs that allows nodes with slower moving speed to sleep more without losing the network connectivity. Theoretical analysis shows that the Uni-scheme can render up to 24\% improvement in energy saving as compared with the previous arts.

Many practical applications of classification require the classifier to produce a very low false-positive rate. Although the Support Vector Machine (SVM) has been widely applied to these applications due to its superiority in handling high dimensional data, there are relatively little effort other than setting a threshold or changing the costs of slacks to ensure the low false-positive rate. In this paper, we propose the notion of Asymmetric Support VectorMachine (ASVM) that takes into account the false-positives and the user tolerance in its objective. Such a new objective formulation allows us to raise the confidence in predicting the positives, and therefore obtain a lower chance of false-positives. We study the effects of the parameters in ASVM objective and address some implementation issues related to the Sequential Minimal Optimization (SMO) to cope with large-scale data. An extensive simulation is conducted and shows that ASVM is able to yield either noticeable improvement in performance or reduction in training time as compared to the previous arts.

Quorum-based power saving (QPS) protocols have been proposed for ad hoc networks (e.g., IEEE 802.11 ad hoc mode) to increase energy efficiency and prolong the operational time of mobile stations. These protocols assign to each station a cycle pattern that specifies when the station should wake up (to transmit/receive data) and sleep (to save battery power). In all existing QPS protocols, the cycle length is either identical for all stations or is restricted to certain numbers (e.g. squares or primes). These restrictions on cycle length severely limit the practical use of QPS protocols as each individual station may want to select a cycle length that is best suited for its own need (in terms of remaining battery power, tolerable packet delay, and drop ratio). In this paper we propose the notion of hyper quorum system (HQS)-a generalization of QPS that allows for arbitrary cycle lengths. We describe algorithms to generate two different classes of HQS given any set of arbitrary cycle lengths as input. We then present analytical and simulation results that show the benefits of HQS-based power saving protocols over the existing QPS protocols.

International audience; Repairing techniques for relational databases have leveraged in- tegrity constraints to detect and then resolve errors in the data. User guidance has started to be employed in this setting to avoid a prohibitory exploration of the search space of solutions. In this pa- per, we present a user-guided repairing technique for Knowledge Bases (KBs) enabling updates suggested by the users to resolve errors. KBs exhibit more expressive constraints with respect to relational tables, such as tuple-generating dependencies (TGDs) and negative rules (a form of denial constraints). We consider TGDs and a notable subset of denial constraints, named contra- diction detecting dependencies (CDDs). We propose user-guided polynomial-delay algorithms that ensure the repairing of the KB in the extreme cases of interaction among these two classes of constraints. To the best of our knowledge, such interaction is so far unexplored even in repairing methods for relational data. We prove the correctness of our algorithms and study their feasibility in practical settings. We conduct an extensive experimental study on synthetically generated KBs and a real-world inconsistent KB equipped with TGDs and CDDs. We show the practicality of our proposed interactive strategies by measuring the actual delay time and the number of questions required in our interactive framework.