Baidu USA LLC

Google Inc.

Baidu.com Times Technology (Beijing) Co. Ltd.

Kang Li

Zhenyu Zhong

Zhisheng Hu

Yueqiang Cheng

Yun Liu

David Coz

Robert Carter Dunn

Xinyang Zhang

Jessica Gallegos

Mengyuan Li

Shengjian Guo

Ayush Jain

Dennis Ai

Huibo Wang

Yuan Liu

Clara Eng

David Henry Way

Peggy Yen Phuong Bui

Yinqian Zhang

Vishakha Gupta

TLB poisoning attacks take advantage of security issues of translation lookaside buffer (TLB) management on SEV processors in Secure Encrypted Virtualization (SEV) virtual machines (VMs). In various embodiments, a hypervisor may poison TLB entries between two processes of a SEV VM to compromise the integrity and confidentiality of the SEV VM. Variants of TLB poisoning attacks and end-to-end attacks are shown to be successful on both Advanced Micro Devices (AMD) SEV and SEV-Encrypted State (SEV-ES). Countermeasures for thwarting TLB poisoning attacks include hardware-enforced TLB flush processes and re-exec schemes that, among other things, prevent attackers from manipulating TLB entries and causing a privileged victim process to execute malicious code in an attempt to bypass a password authentication.

Translation lookaside buffer (TLB) poisoning attacks on secure encrypted virtualization

Provided are a data processing method and apparatus, an electronic device, and a medium, and relates to the technical field of computers, and in particular to the field of autonomous driving and intelligent transportation. An implementation is: obtaining risk information in driving scenario data of a driving scenario; determining a risk factor set based on the risk information, where the risk factor set includes a risk factor existing in the driving scenario data; and generating a simulated risk scenario based on the risk factor set, where the simulated risk scenario reflects at least one risk factor in the risk factor set.

Data processing

The present disclosure is directed to a deep learning system for differential diagnoses of skin diseases. In particular, the system performs a method that can include obtaining a plurality of images that respectively depict a portion of a patient's skin. The method can include determining, using a machine-learned skin condition classification model, a plurality of embeddings respectively for the plurality of images. The method can include combining the plurality of embeddings to obtain a unified representation associated with the portion of the patient's skin. The method can include determining, using the machine-learned skin condition classification model, a skin condition classification for the portion of the patients skin, the skin condition classification produced by the machine-learned skin condition classification model by processing the unified representation, wherein the skin condition classification identifies one or more skin conditions selected from a plurality of potential skin conditions.

Deep learning system for differential diagnosis of skin diseases

The disclosure describes various embodiments for detecting an unexpected control state of an autonomous driving system. According to an embodiment, an exemplary method of detecting an unexpected control state of an autonomous driving system include the operations of generating environmental data of a vehicle; determining, by the autonomous driving system, a first control state based on the environmental data of the vehicle; determining, by a reference model, a second control state based on the environmental data, wherein the reference model defines at least one scenario each corresponding to a plurality of expected control states and a state switching condition, and in each of the expected control states corresponding to the scenario, an action of the vehicle in the scenario obeys a traffic rule; and determining the unexpected control state of the autonomous driving system by comparing the first control state with the second control state.

Method and apparatus for detecting unexpected control state in autonomous driving system

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Sunnyvale, CA, United States of America

Kang Li