Visual (V) surveillance systems are extensively deployed and becoming the largest source of big data. On the other hand, electronic (E) data also plays an important role in surveillance and its amount increases explosively with the ubiquity of mobile devices. One of the major problems in surveillance is to determine human objects' identities among different surveillance scenes. Traditional way of processing big V and E datasets separately does not serve the purpose well because V data and E data are imperfect alone for information gathering and retrieval. Matching human objects in the two datasets can merge the good of the two for efficient large-scale surveillance. Yet such matching across two heterogeneous big datasets is challenging. In this paper, we propose an efficient set of parallel algorithms, called EV-Matching, to bridge big E and V data. We match E and V data based on their spatiotemporal correlation. The EV-Matching algorithms are implemented on Apache Spark to further accelerate the whole procedure. We conduct extensive experiments on a large synthetic dataset under different settings. Results demonstrate the feasibility and efficiency of our proposed algorithms.

Massive visual traffic data have become available recently. Though it opens the realm of intelligent traffic analysis, processing the data in a timely manner is difficult yet critical to time sensitive decisions, which are typical to traffic related management. In this paper, we study time-bounded aggregation analytics on large visual traffic data including traffic images and videos. We first find that current MapReduce framework can not work well due to two challenges: first, significant dual diversities exist on data distributions and processing time; second, apriori knowledge on these distributions and time costs are not always available. However, we also observe spatial and temporal locality on data values and processing time. Based on the examination, we design Traffic At-a-Glance (TaG), an augmented MapReduce framework for time-bounded traffic analytics jobs. Particularly, we propose a novel sampling algorithm that exploits traffic data localities and stratifies samples based on data distributions and processing time. It runs in an iterative, adaptive manner without apriori knowledge. Moreover, we propose a heuristic scheduling algorithm with considerations of batch processing overhead. Further, we refine the load balancing mechanism based on data processing time locality to respect job time bounds. In addition, we extend TaG to well handle traffic videos by sampling video data based on motion information encoded in the videos. We implement TaG on Hadoop and conduct extensive experiments on a large visual traffic dataset. The evaluations on different data sizes show TaG is able to achieve high accuracy within time bounds.

Massive visual traffic data have become available recently, which provides an opportunity for intelligent traffic analysis. Timely processing is particularly necessary for traffic analysis. In this paper, we study time-bounded aggregation analytics on large visual traffic data. We first find that current MapReduce framework can not work well due to two challenges: first, significant dual diversities exist on data distributions and processing time; second, no apriori knowledge on these distributions and time costs is available. However, we also observe spatial and temporal locality on data values and processing time. Based on the examination, we design TaG, an augmented MapReduce framework for time-bounded traffic analytics jobs. Particularly, we propose a novel sampling algorithm that exploits traffic data localities and stratifies samples based on data distributions and processing time. It runs in an iterative, adaptive manner without apriori knowledge. Moreover, we propose a heuristic scheduling algorithm with considerations of batch processing overhead. Further, we refine load balancing mechanism based on data processing time locality to respect job time bounds. We implement TaG on Hadoop and conduct extensive experiments on a large traffic image dataset. The evaluations on different data sizes show TaG is able to achieve high accuracy within different time bounds.

Abstract Location‐related mobile social network services are popular nowadays, and their methods to obtain end users’ location information are based on people's self‐report location claims, using mobile devices to check positions and send them back to the service providers. However, this mechanism has a serious vulnerability that makes malicious users be able to access restricted resource by transmitting fake locations. Both academic and industrial researchers are recently aware of this problem's importance since the commercialized trend of location‐related mobile social network services. To address this issue, we propose mobile phone‐based physical‐social location, a mobile phone‐based location proof system to verify users’ location claims and defend various fake location information. Our core idea is that a user's location claim can be proved by a set of selective physical encountered people serving as “witnesses” who are co‐located with him/her in that area. The system is composed of proof generation and verification. In the proof generation phase, we leverage a certain number of co‐located people to generate certificates as location proofs during their encounters via bluetooth interface. In the verification phase, we propose an efficient verification scheme to make our system accurate and adaptive. We have implemented the MPSL system using real world Nokia N82 (Nokia, Espoo, Finland) phones. Our experimental results show that our mobile phone‐based system can achieve high verification accuracy and good performance. Copyright © 2014 John Wiley & Sons, Ltd.

Barrier coverage in visual camera sensor networks (visual barrier coverage) has important real-world applications like battlefield surveillance, environmental monitoring, and protection of government property. Cost-effective deployment, a fundamental issue of visual barrier coverage, considers how to deploy the fewest camera sensors along the barrier to detect intruders (e.g., capture faces) with desirable performance. Existing visual barrier coverage approaches like full-view coverage require numerous camera sensors for capturing intruders' faces deterministically for any trajectory and facing angle. However, intruders' trajectories and facing angles are bounded and deterministic intruder detection requires many camera sensors for rare intrusion cases. Certain practical applications can tolerate limited intrusion mis-detection given budget limitations. This paper proposes local face-view barrier coverage, a novel concept that achieves statistical barrier coverage in camera sensor networks leveraging intruders' trajectory lengths ℓ along the barrier and head rotation angles δ. Using (ℓ, δ) and other parameters, we derive a rigorous probability bound for intruder detection for local face-view barrier coverage via a feasible deployment pattern. Our detection probability bound and deployment pattern can guide practical camera sensor network deployments with camera sensor budgets. Extensive evaluations show that local face-view barrier coverage requires up to 50% fewer camera sensors than full-view barrier coverage.

Human tracking in video has many practical applications such as visual guided navigation, assisted living, etc. In such applications, it is necessary to accurately track multiple humans across multiple cameras, subject to real-time constraints. Despite recent advances in visual tracking research, the tracking systems purely relying on visual information fail to meet the accuracy and real-time requirements at the same time. In this paper, we present a novel accurate and real-time human tracking system called VM-Tracking. The system aggregates the information of motion (M) sensor on human, and integrates it with visual (V) data based on physical locations. The system has two key features, i.e. location-based VM fusion and appearance-free tracking, which significantly distinguish itself from other existing human tracking systems. We have implemented the VM-Tracking system and conducted comprehensive experiments on challenging scenarios.

Electronic channel sounding plays a vital role in developing wireless communication systems. It is critical for transceivers' equalization and filtering operations. However, current pure electronic channel sounding techniques are not well-suited for emerging scenarios such as opportunistic spectrum access, channel impulse response (CIR) based wireless positioning, and wireless security applications, which demand rapid, high-resolution, and spectrum agile channel measurements on commercial off-the-shelf (COTS) devices. To address these critical issues, this paper proposes EV-Sounding, a novel methodology for visual assisted electronic channel sounding. Based on frequency domain channel sounding, EV-Sounding leverages cameras for visual estimation of sparsity locations to reduce the number of frequency samples, thus speeding up the sounding process. EV-Sounding achieves both high-resolution CIR measurements and spectrum agility. We prototype an EV-Sounding system on COTS devices. Our real-world experimental results and extensive simulations validate EV-Sounding's performance.

In this paper, we propose E-Shadow, a distributed mobile phone-based local social networking system. E-Shadow has two main components: (1) Local profiles. They enable E-Shadow users to record and share their names, interests, and other information with fine-grained privacy controls. (2) Mobile phone based local social interaction tools. E-Shadow provides mobile phone software that enables rich social interactions. The software maps proximate users’ local profiles to their human owners and enables user communication and content sharing. We have designed and implemented E-Shadow on mobile phones. In our E-Shadow system, we allow users to perform dynamic and layered information publishing, making use of interpersonal relevance in space and time. Our system also provides a mechanism to help users perform direction-driven localization of an E-Shadow and match it with its owner. Experiments on real world Windows Mobile phones and large-scale simulations show that our system disseminates information efficiently and helps receivers find the direction of a specific E-Shadow with accuracy. We believe our E-Shadow concept and system can lead to a more tightly-knit temporary community in one’s physical vicinity.

Recent years have witnessed mass proliferation of mobile devices with rich wireless communication capabilities as well as emerging mobile device-based information dissemination applications that leverage these capabilities. This paper proposes TurfCast, a novel information dissemination service that selectively broadcasts information in particular "turfs,â abstract logical spaces in which receivers are situated. Such turfs can be temporal or spatial based on receivers' lingering time or physical areas, respectively. TurfCast has many applications such as electronic proximity advertising and mobile social networking. To enable TurfCast, we propose two supporting technologies: TurfCode and TurfBurst. TurfCode is a nested 0-1 fountain code that enables the broadcaster to transmit either all information or none at all to receivers. TurfBurst exploits the Shannon bound to differentiate among receivers: those who cannot receive information fast enough receive none at all, even if they linger near the broadcaster. We implement TurfCast on real-world devices and conduct experiments in both indoor and outdoor environments. Our experimental results illustrate TurfCast's potential for controlling information dissemination in wireless networks.

Nowadays, more and more objects can be represented with electronic identifiers, e.g., people can be recognized from their laptops' MACs, and products can be identified by their RFID numbers. Localizing electronic identifiers is more and more important for a fully digitalized life. However, traditional wireless localization techniques are not satisfactory in performance to determine these electronic identifiers' positions. Some of them require costly hardware to achieve high accuracy and, hence, are not practical. The others are inaccurate and not robust against environmental noises, e.g., RSSI-based localization. Therefore, an accurate and practical approach for localizing electronic identifiers is needed. In this paper, we propose a new localization technique called EV-Loc. In EV-Loc, we make use of visual signals to help improve the accuracy of wireless localization. Our technique fully takes advantage of the high accuracy of visual signals and pervasiveness of electronic signals. To effectively couple these two signals together, we have designed an E-V match engine to find the correspondence between an object's electronic identifier and its visual appearance. We have implemented our technique on mobile devices and evaluated it in the real world. The localization error is less than 1 m. We have also evaluated our approach using large-scale simulations. The results show that our approach is accurate and robust.

In this article, we address a new unexplored problem: what are the optimal patterns to achieve connected coverage in wireless networks with directional antennas. As their name implies, directional antennas can focus their transmission energy in a certain direction. This feature leads to lower cross-interference and larger communication distance. It has been shown that, with proper scheduling mechanisms, directional antennas may substantially improve networking performance in wireless networks. In this article, we propose a set of deployment patterns to achieve full coverage and up to 2-connectivity under two different antenna models, namely the sector model and the knob model. These patterns are optimal under most combinations of communication and sensing ranges. We also introduce with detailed analysis several fundamental theorems and conjectures. Finally, we examine a more realistic physical model, where there might be strong interference and both the sensing range and the communication range might be irregular. The results show that our designed patterns work well even in unstable and fickle physical environments.

In this paper, we propose a new type of incentive-driven and privacy-preserving systems for large-scale message dissemination in mobile networks. To distribute incentives which encourage forwarding behaviors, such as monetary rewards, we want to keep track of the forwarder list. In our algorithms, we rely on a Probabilistic one-ownership forwarding algorithm to record the list, so that the exchanged messages can be kept short and privacy preserving. More specifically, only one hop of forwarder information, instead of the complete list, is recorded, and the information is updated probabilistically following two ownership flipping models, namely, One-Flip and Always-Flip models. We also use a Bluetooth Service Discovery Protocol (SDP) toolkit to enable fast, configuration-free message exchange. Throughout the paper, we use coupon as a typical type of message to illustrate the core ideas. We have implemented the coupon dissemination system in Java ME. Our experiments on real-world mobile phones, such as Nokia and Samsung phones, and large-scale simulations show that our system is efficient in peer-to-peer message distribution and capable of massive deployment. We believe our key methodology can serve as a general framework for facilitating information propagation on mobile phones, where incentives and privacy protection are both essential.

Human localization is an enabling technology for many mobile applications. As more and more people carry mobile phones with them, we can now localize a person by localizing his mobile phone. However, it is observed that presence of human bodies introduces heavy interference to mobile phone signals. This has been one of the major causes of inaccurate wireless localization for humans. In this paper, we propose using video cameras to help estimate human body's interference on mobile device's signals. We combine human orientation detection and human/phone/AP relative position inference estimation to better measure how a human blocks or reflects wireless signals. We have also developed a signal distortion compensation model. Based on these technologies, we have implemented a human localization system called EV-Human. Real world experiments show that our EV-system can accurately and robustly localize humans.

In this paper, we study the problem of wireless coverage in bounded areas. Coverage is one of the fundamental requirements of wireless networks. There has been considerable research on optimal coverage of infinitely large areas. However, in the real world, the deployment areas of wireless networks are always geographically bounded. It is a much more challenging and significant problem to find optimal deployment patterns to cover bounded areas. In this paper, we approach this problem starting from the development of tight lower bounds on the number of nodes needed to cover a bounded area. Then we design several deployment patterns for different kinds of convex and concave shapes such as rectangles and L-shapes. These patterns require only few more nodes than the theoretical lower bound, and can achieve efficient coverage. We have also carefully addressed and evaluated practical conditions such as coverage modeling and connectivity regarding our deployment patterns.

Abstract—Wide range wireless networks often suffer from annoying service deterioration due to fickle wireless environment. This is especially the case with passengers on long distance train (LDT) to connect onto the Internet. To improve the service quality of wide range wireless networks, we present the DragonNet protocol with its implementation. The DragonNet system is a chained gateway which consists of a group of interlinked DragonNet routers working specifically for mobile chain transport systems. The protocol makes use of the spatial diversity of wireless signals that not all spots on a surface see the same level of radio frequency radiation. In the case of a LDT of around 500 meters, it is highly possible that some of the spanning routers still see sound signal quality, when the LDT is partially blocked from wireless Internet. DragonNet protocol fully utilizes this feature to amortize single point router failure over the whole router chain by intelligently rerouting traffics on failed ones to sound ones. We have implemented the DragonNet system and tested it in real railways over a period of three months. Our results have pinpointed two fundamental contributions of DragonNet protocol. First, DragonNet significantly reduces average temporary communication blackout (i.e. no Internet connection) to 1.5 seconds compared with 6 seconds that without DragonNet protocol. Second, DragonNet efficiently doubles the aggregate throughput on average.

Recent years have witnessed mass proliferation of mobile devices with rich wireless communication capabilities as well as emerging mobile device based information dissemination applications that leverage these capabilities. This paper proposes TurfCast, a novel information dissemination service that selectively broadcasts information in particular “turfs,” abstract logical spaces in which receivers are situated. Such turfs can be temporal or spatial based on receivers' lingering time or physical areas, respectively. TurfCast has many applications such as electronic proximity advertising and mobile social networking. To enable TurfCast, we propose two supporting technologies: TurfCode and TurfBurst. TurfCode is a nested 0-1 fountain code that enables the broadcaster to transmit either all information or none at all to receivers. TurfBurst exploits the Shannon bound to differentiate among receivers: those who cannot receive information fast enough receive none at all, even if they linger near the broadcaster. We implement TurfCast on real-world devices and conduct experiments in both indoor and outdoor environments. Our experimental results illustrate TurfCast's potential for controlling information dissemination in wireless networks.

Video cameras have been deployed at almost every critical location, and they keep generating huge volumes of video data. The current visual processing technologies are not efficient in handling all these data for surveillance purposes, and a large amount of human power is needed to process them. In this paper, we propose the E-V system, which uses electronic footprints to help sort through this swamp of data. Electronic footprints are wireless signals emitted by mobile devices carried by people. They are ubiquitous and amenable to collection and indexing. We study how to use electronic footprints to help quickly and accurately identify object's appearance model from large volumes of video data. We have formulated the problem and provided efficient algorithms to achieve the identification on large data sets. Real world experiments and large-scale simulations have been done, which confirms the feasibility and efficiency of the proposed algorithms.

Nowadays, an increasing number of objects can be represented by their wireless electronic identifiers. For example, people can be recognized by their phone numbers or their phones' WiFi' MAC addresses and products can be identified by their RFID numbers. Localizing objects with electronic identifiers is increasingly important as our lives become increasingly "digitalized". However, traditional wireless localization techniques cannot meet the fast growing needs of accurate and cost efficient localization. Some of these techniques require expensive hardware to achieve high accuracy, which is impractical for massive deployment. Others, such as WiFi RSSI based localization, are inaccurate and not robust to environmental noise. In this paper, we propose a new localization technique called EV-Loc. In EV-Loc, we use visual signals to help improve the accuracy of wireless localization. Our technique fully leverages visual signals' high accuracy and electronic signals' pervasiveness. To effectively couple these two signals, we design an E-V match engine to find the correspondence between an object's electronic identifier and its visual appearance. We implement our technique on mobile devices and evaluate it in real-world scenarios. The localization error is less than 1 m. We also evaluate our approach using large scale simulations. The results show that our approach is accurate and robust.

This paper presents an empirical study on the performance of mobile High Speed Packet Access (a 3.5G cellular standard usually abbreviated as HSPA) networks in Hong Kong via extensive field tests. Our study, from the viewpoint of end users, covers virtually all possible mobile scenarios in urban areas, including subways, trains, off-shore ferries, and city buses. We have confirmed that mobility has largely negative impacts on the performance of HSPA networks, as fast-changing wireless environment causes serious service deterioration or even interruption. Meanwhile, our field experiment results have shown unexpected new findings and thereby exposed new features of the mobile HSPA networks, which contradict commonly held views. We surprisingly find out that mobility can improve fairness of bandwidth sharing among users and traffic flows. Also, the triggering and final results of handoffs in mobile HSPA networks are unpredictable and often inappropriate, thus calling for fast reacting fallover mechanisms. Moreover, we find that throughput performance does not monotonically decrease with increased mobility level. We have conducted in-depth research to furnish detailed analysis and explanations to what we have observed. We conclude that mobility is a double-edged sword for HSPA networks. To the best of our knowledge, this is the first public report on a large-scale empirical study on the performance of commercial mobile HSPA networks.

In this paper, we propose E-Shadow, a distributed mobile phone-based local social networking system. E-Shadow has two main components: (1) Local profiles. They enable EShadow users to record and share their names, interests, and other information with fine-grained privacy controls. (2) Mobile phone based local social interaction tools. E-Shadow provides mobile phone software that enables rich social interactions. The software maps proximate users' local profiles to their human owners and enables user communication and content sharing. We have designed and implemented E-Shadow on mobile phones. In our E-Shadow system, we allow users to perform dynamic and layered information publishing, making use of interpersonal relevance. Our system also provides a mechanism to help users perform direction-driven localization of an E-Shadow and match it with its owner. Experiments on real world Windows Mobile phones and large-scale simulations show that our system disseminates information efficiently and helps receivers find the direction of a specific E-Shadow with accuracy. We believe our E-Shadow concept and system can lead to a more tightly-knit temporary community in one's physical vicinity.

Wide range wireless networks often suffer from annoying service deterioration due to fickle wireless environment. This is especially the case with passengers on long distance train (LDT) to connect onto the Internet. To improve the service quality of wide range wireless networks, we present the DragonNet protocol with its implementation. The DragonNet system is a chained gateway which consists of a group of interlinked DragonNet routers working specifically for mobile chain transport systems. The protocol makes use of the spatial diversity of wireless signals that not all spots on a surface see the same level of radio frequency radiation. In the case of a LDT of around 500 meters, it is highly possible that some of the spanning routers still see sound signal quality, when the LDT is partially blocked from wireless Internet. DragonNet protocol fully utilizes this feature to amortize single point router failure over the whole router chain by intelligently rerouting traffics on failed ones to sound ones. We have implemented the DragonNet system and tested it in real railways over a period of three months. Our results have pinpointed two fundamental contributions of DragonNet protocol. First, DragonNet significantly reduces average temporary communication blackout (i.e. no Internet connection) to 1.5 seconds compared with 6 seconds that without DragonNet protocol. Second, DragonNet efficiently doubles the aggregate throughput on average.

In this paper, we address a new unexplored problem - what are the optimal patterns to achieve connected coverage in wireless networks with directional antennas. As their name implies, directional antennas can focus their transmission energy in a certain direction. This feature leads to lower cross-interference and larger communication distance. It has been shown that with proper scheduling mechanisms, directional antennas may substantially improve networking performance in wireless networks. In this paper, we propose a set of optimal patterns to achieve full coverage and global connectivity under two different antenna models, i.e., the sector model and the knob model. We also introduce with detailed analysis several fundamental theorems and conjectures. Finally, we examine a more realistic physical model, where there might be strong interference, and both the sensing range and the communication range might be irregular. The results show that our designed patterns work well even in unstable and fickle physical environments.

In this paper, we propose P3-coupon, a Prompt and Privacy-preserving electronic coupon distribution system based on a Probabilistic one-ownership forwarding algorithm. In this algorithm, only one hop of forwarder (coupon owner) information instead of the complete forwarder list is recorded to keep the coupon short and privacy-preserving. Such information is updated probabilistically following two ownership flipping models, namely, One-Flip and Always-Flip models. We also use a Bluetooth Service Discovery Protocol (SDP) toolkit to enable fast, configuration-free coupon exchange. We have implemented the system in Java ME. Our experiments on real world mobile phones, such as Nokia and Samsung phones, and large scale simulations show that our system is efficient in peer to peer coupon distribution and capable of massive deployment. We believe our key methodology can serve as a general framework for facilitating information propagation on mobile phones, which requires promptness and privacy protection.

This paper presents an empirical study on the performance of mobile High Speed Packet Access (HSPA, a 3.5G cellular standard) networks in Hong Kong via extensive field tests. Our study, from the viewpoint of end users, covers virtually all possible mobile scenarios in urban areas, including subways, trains, off-shore ferries and city buses. We have confirmed that mobility has largely negative impacts on the performance of HSPA networks, as fast-changing wireless environment causes serious service deterioration or even interruption. Meanwhile our field experiment results have shown unexpected new findings and thereby exposed new features of the mobile HSPA networks, which contradict commonly held views. We surprisingly find out that mobility can improve fairness of bandwidth sharing among users and traffic flows. Also the triggering and final results of handoffs in mobile HSPA networks are unpredictable and often inappropriate, thus calling for fast reacting fallover mechanisms. We have conducted in-depth research to furnish detailed analysis and explanations to what we have observed. We conclude that mobility is a double-edged sword for HSPA networks. To the best of our knowledge, this is the first public report on a large scale empirical study on the performance of commercial mobile HSPA networks.

Low-connectivity and full-coverage three dimensional Wireless Sensor Networks (WSNs) have many real-world applications. By low connectivity, we mean there are at least k disjoint paths between any two sensor nodes in a WSN, where k ≤ 4. In this paper, we design a set of patterns to achieve 1-, 2-, 3- and 4-connectivity and full-coverage, and prove their optimality under any value of the ratio of communication range rcover sensing range rs, among regular lattice deployment patterns. We further investigate the evolutions among all the proposed low-connectivity patterns. Finally, we study the proposed patterns under several practical settings.

802.11 wireless networks have gained ever greater popularity nowadays. Apart from static wireless connections, people begin to expect more user-friendly features from this kind of networks, such as support for seamless roaming. In this paper, we study the handoff process in large AP-dense 802.11 networks, which is one of the most common forms of WiFi under usage. A series of field experiments are carried out and some critical handoff parameters are evaluated. With some newly discovered features, i.e. differentiated probe response time and rich AP information hidden in wireless traffic, we have managed to significantly improve the essential process of AP scan, a bottleneck towards fast and smooth handoffs. The solution is collectively called D-Scan (Scan in AP-Dense 802.11 networks). Real experiments are conducted to show the superiority of our solution.

In this paper, we introduce a new type of coverage for wireless sensor networks, called Directed Coverage (D-coverage). Basically, D-coverage is the coverage provided by a sensor network monitoring an area between two boundaries, through which the intruder attempts to penetrates the area. We also study how to measure the quality of D-coverage. Our first evaluation approach is a projection-based simple approach, while our second approach is a more comprehensive Markov chain based approach. Our evaluation approaches can accurately evaluate the quality and provide good guidelines for sensor network deployment and run-time repair.

Low-connectivity and full-coverage three dimensional Wireless Sensor Networks (WSNs) have many real-world applications. By low connectivity, we mean there are at least k disjoint paths between any two sensor nodes in a WSN, where k ≤ 4. In this paper, we design a set of patterns for these networks. In particular, we design and prove the optimality of 1- and 2-connectivity patterns under any value of the ratio of communication range rc over sensing range rs, among regular lattice deployment patterns. We further propose a set of patterns to achieve 3- and 4-connectivity patterns and investigate the evolutions among all the proposed low-connectivity patterns. Finally, we study the proposed patterns under several practical settings.

In this paper, we investigate video-based vulnerabilities in 3G Smartphones. Particularly, we design a new video-based spyware, called Stealthy Video Capturer (SVC). SVC can secretly record video information for the third party, greatly compromising Smartphone users' privacy. We implement the spyware and conduct extensive experiments on real world 3G Smartphones. Our experimental results show that the spyware can capture private video information with unremarkable power consumption, CPU and memory occupancy, hence being stealthy to Smartphone users. Moreover, SVC can naturally be resistant to almost all commercial anti-virus tools, like McAfee, Kaspersky and F-Secure mobile version. To the best of our knowledge, our work is the first one to address video-based vulnerabilities in 3G Smartphones. We expect our work will prompt serious attentions on this issue.