DYNAMIC SECURITY POLICY ENFORCEMENT METHOD FOR CONTAINER SYSTEM, RECORDING MEDIUM AND SYSTEM FOR PERFORMING THE SAME

    公开(公告)号:US20230362198A1

    公开(公告)日:2023-11-09

    申请号:US18135593

    申请日:2023-04-17

    CPC classification number: H04L63/20 H04L63/0263 H04L63/1416

    Abstract: Provided is a dynamic security policy enforcement system for a container system. The dynamic security policy enforcement system comprises a policy management unit for generating and managing a security policy for a container based on a structured format including a set of rules of a predetermined condition; a policy enforcement unit for checking the set of rules when the container requests a system call, changing the security policy of the structured format into a code in a preset format, and transferring the policy changed into the code to a kernel space; and a policy operation decision unit for enforcing the policy received from the policy enforcement unit in the kernel space based on a policy enforcement program that hooks the system call and generating a return value for performing a predetermined operation. Due to this, a policy can be applied to containers in all states including an initialization state and a running state at any time, and there is no need to restart the system or container to apply the policy.

    METHOD AND APPARATUS FOR PATCH GAN-BASED DEPTH COMPLETION IN AUTONOMOUS VEHICLES

    公开(公告)号:US20230230265A1

    公开(公告)日:2023-07-20

    申请号:US18098940

    申请日:2023-01-19

    CPC classification number: G06T7/55 G06V20/56 G06N3/0475 G06T2207/20084

    Abstract: Provided are a patch GAN-based depth completion method and apparatus in an autonomous vehicle. The patch-GAN-based depth completion apparatus according to the present invention comprises a processor; and a memory connected to the processor, wherein the memory stores program instructions executable by the processor for performing operations in a generating unit of a generative adversarial neural network comprising a first branch and a second branch based on an encoder-decoder comprising receiving an RGB image and a sparse image through a camera and LiDAR, generating a dense first depth map by processing color information of the RGB image through the first branch, generating a dense second depth map by up-sampling the sparse image through the second branch, generating a dense final depth map by fusing the first depth map and the second depth map, and determining, by a discriminating unit of the generative adversarial neural network, whether the final depth map is fake or real by dividing the final depth map and depth measurement data into a plurality of patches.

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