Over 500,000 Car Tracking Devices' Passwords Accidentally Leaked Due to Misconfigured Cloud Server > 자유게시판

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In one more case of an unintentional information leak, login credentials of over 500,000 car monitoring gadgets were freely uncovered as a result of a misconfigured cloud server. SVR permits its prospects to trace their vehicles spherical the clock, so they can monitor and recover them in case their vehicle has been stolen. The agency attaches a tracking device to a car in a discreet location, so if the automobile is stolen, an unknown driver would have no data of it being monitored. Based on researchers at Kromtech Security, who found the breach, the info uncovered included SVR customers' account credentials, akin to emails and passwords. Users' automobile knowledge, including VIN numbers and licence plates were also freely uncovered. The information was exposed by way of an insecure Amazon S3 bucket. Kromtech researcher Bob Diachenko stated in a weblog. SVR's automobile tracking device monitors everywhere a car has been for iTagPro support the past 120 days, which might be easily accessed by anyone who has entry to users' login credentials. The insecure Amazon S3 bucket has been secured, after Kromtech reached out to SVR and notified them about the breach. It still stays unclear as to how lengthy the information remained freely uncovered. Additionally it is unsure whether or not the info was possibly accessed by hackers.

Legal status (The legal status is an assumption and isn't a authorized conclusion. Current Assignee (The listed assignees could also be inaccurate. Priority date (The priority date is an assumption and isn't a authorized conclusion. The appliance discloses a goal monitoring methodology, a target tracking device and electronic tools, and relates to the technical subject of artificial intelligence. The tactic includes the following steps: a primary sub-network within the joint monitoring detection community, a primary characteristic map extracted from the target function map, and a second function map extracted from the target feature map by a second sub-network in the joint monitoring detection community; fusing the second feature map extracted by the second sub-network to the primary feature map to acquire a fused feature map corresponding to the first sub-network; buying first prediction data output by a primary sub-community based on a fusion feature map, and buying second prediction info output by a second sub-network; and figuring out the present position and the movement path of the moving target within the target video based on the primary prediction info and the second prediction information.

The relevance among all the sub-networks that are parallel to each other can be enhanced via characteristic fusion, and the accuracy of the determined place and movement path of the operation goal is improved. The present software relates to the sphere of synthetic intelligence, and specifically, to a goal monitoring method, apparatus, and digital device. In recent times, artificial intelligence (Artificial Intelligence, AI) know-how has been broadly utilized in the sphere of target tracking detection. In some eventualities, a deep neural network is typically employed to implement a joint trace detection (monitoring and object detection) network, where a joint trace detection network refers to a network that is used to attain goal detection and goal hint collectively. In the prevailing joint tracking detection community, the position and movement path accuracy of the predicted transferring goal isn't high enough. The application supplies a target tracking methodology, iTagPro support a target tracking device and electronic gear, which may improve the issues.

In one side, an embodiment of the present utility offers a target monitoring technique, the place the tactic includes: a first sub-network in a joint monitoring detection network is used for extracting a first characteristic image from a target characteristic image, and a second sub-network in the joint tracking detection network is used for extracting a second characteristic picture from the goal characteristic picture, whereby the goal characteristic picture is extracted from a video body of a target video; fusing the second characteristic map extracted by the second sub-community to the first characteristic map to obtain a fused characteristic map corresponding to the primary sub-community; acquiring first prediction data output by a first sub-community in keeping with the fusion feature map, and acquiring second prediction info output by a second sub-community; based mostly on the primary prediction info and the second prediction info, determining the present place and the motion path of the shifting goal within the target video.