Xu, Haifeng (University of Southern California) | Wang, Kai (University of Southern California) | Vayanos, Phebe (University of Southern California) | Tambe, Milind (University of Southern California)
Traditional security games concern the optimal randomized allocation of human patrollers, who can directly catch attackers or interdict attacks. Motivated by the emerging application of utilizing mobile sensors (e.g., UAVs) for patrolling, in this paper we propose the novel Sensor-Empowered security Game (SEG) model which captures the joint allocation of human patrollers and mobile sensors. Sensors differ from patrollers in that they cannot directly interdict attacks, but they can notify nearby patrollers (if any). Moreover, SEGs incorporate mobile sensors' natural functionality of strategic signaling. On the technical side, we first prove that solving SEGs is NP-hard even in zero-sum cases. We then develop a scalable algorithm SEGer based on the branch-and-price framework with two key novelties: (1) a novel MILP formulation for the slave; (2) an efficient relaxation of the problem for pruning. To further accelerate SEGer, we design a faster combinatorial algorithm for the slave problem, which is provably a constant-approximation to the slave problem in zero-sum cases and serves as a useful heuristic for general-sum SEGs. Our experiments demonstrate the significant benefit of utilizing mobile sensors.
A team of researchers at JD AI Research and Beijing University have recently developed a progressive vehicle search system for video surveillance networks, called PVSS. Their system, presented in a paper pre-published on arXiv, can effectively search for a specific vehicle that appeared in surveillance footage. Vehicle search systems could have many useful applications, including enabling smarter transportation and automated surveillance. Such systems could, for instance, allow users to input a query vehicle, search area and time interval to find out where the vehicle was located at different times during the day. Existing vehicle search methods typically assume that all vehicle images are cropped well from surveillance videos, using visual attributes or license plate numbers to identify the target vehicle within these images.
There are few processes in life as nerve-wracking and tedious as going through security at an airport. Whether it's adhering to Transportation Security Administration (TSA) rules of removing laptops from bags, or navigating the seemingly endless, winding queue, getting screened before a flight is time-consuming. But with the help of the Department of Homeland Security (DHS), researchers are working on integrating video surveillance with artificial intelligence (AI) to make this vital security process much smoother. The development new technology to streamline airport security has stagnated in recent decades. A lack of innovation, coupled with a need for increased screening in the wake of events like the 9/11 attacks, have only made the process worse.
CylancePROTECT will be integral to the new Razberi CameraDefense solution that, combined with Razberi's secure appliance architecture, provides comprehensive protection over the server, video management systems (VMS), and camera ecosystem. "The physical and network security worlds continue to converge, putting video surveillance systems and any attached networks at risk from unprotected endpoints," said Tom Galvin, Razberi CEO. "CylancePROTECT is ideal for the Razberi distributed architecture, enabling us to offer our customers the most advanced system for anti-virus protection." CylancePROTECT leverages artificial intelligence to detect and prevent malware from executing on endpoints in real time. Because it uses very little memory and less than one percent of CPU, CylancePROTECT will not disrupt the video management systems running on Razberi ServerSwitchIQ appliances.
This new offering is available to service providers such as security integrators, telecoms and alarm and monitoring companies for reselling to their customers. IvedaAI includes a powerful self-contained server with artificial intelligence (AI) software, capable of searching a combination of objects from dozens to thousands of cameras in less than one second. Video analytics have been around for many years, but adoption has been slow because of inaccuracies and high cost. IvedaAI employs 30 patents in AI, big data analytics and cloud computing. It applies a deep learning algorithm (trained, not programmed), automates processes and uses natural language.