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Full-Text Articles in Physical Sciences and Mathematics
On The Effectiveness Of Using Graphics Interrupt As A Side Channel For User Behavior Snooping, Haoyu Ma, Jianwen Tian, Debin Gao, Chunfu Jia
On The Effectiveness Of Using Graphics Interrupt As A Side Channel For User Behavior Snooping, Haoyu Ma, Jianwen Tian, Debin Gao, Chunfu Jia
Research Collection School Of Computing and Information Systems
Graphics Processing Units (GPUs) are now a key component of many devices and systems, including those in the cloud and data centers, thus are also subject to side-channel attacks. Existing side-channel attacks on GPUs typically leak information from graphics libraries like OpenGL and CUDA, which require creating contentions within the GPU resource space and are being mitigated with software patches. This paper evaluates potential side channels exposed at a lower-level interface between GPUs and CPUs, namely the graphics interrupts. These signals could indicate unique signatures of GPU workload, allowing a spy process to infer the behavior of other processes. We …
Deep Learning On Lie Groups For Skeleton-Based Action Recognition, Zhiwu Huang, C. Wan, T. Probst, Gool L. Van
Deep Learning On Lie Groups For Skeleton-Based Action Recognition, Zhiwu Huang, C. Wan, T. Probst, Gool L. Van
Research Collection School Of Computing and Information Systems
In recent years, skeleton-based action recognition has become a popular 3D classification problem. State-of-the-art methods typically first represent each motion sequence as a high-dimensional trajectory on a Lie group with an additional dynamic time warping, and then shallowly learn favorable Lie group features. In this paper we incorporate the Lie group structure into a deep network architecture to learn more appropriate Lie group features for 3D action recognition. Within the network structure, we design rotation mapping layers to transform the input Lie group features into desirable ones, which are aligned better in the temporal domain. To reduce the high feature …