and has been adopted, in part, by Internet Explorer 8 and, in full, by Google Chrome and the HTML 5 working group. Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays. Commun. The following articles are merged in Scholar. Yinzhi Cao, Vaibhav Rastogi & Yan Chen. Google Scholar Digital Library; Zhuang Qian, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Rui Zhang, and Xinping Yi. [Google Scholar] Preprint. in Cyberspace Security, Excellent Cyberspace Security Students Class, Sichuan University, 2022; GPA: 3.92, rank: 2/172(1%) . Yinzhi Yang, Min Gao, Yuyang Wu, Zheng Liu, Jinpeng Xie, Zhongmin Deng, Guangming Cai, Xinwang Cao & Wei Ke. Finally, we discuss how the texture of adversarial patches can be optimized efficiently using RL by parametrizing the appearance of the patch with a class-specific texture dictionary .
Type "@" in a cell to mention people. In exchange for the increased complexity of the authentication procedure, SSO makes it convenient for users to. Show more. In ACM ASIA Conference on Computer and Communications Security (ASIACCS), 2022. In IEEE Symposium on Security and Privacy (S&P), pages 463--480. Yinzhi Cao, Yun Chen, and Gregory Falco receive award aimed to develop the next generation of academic scientists, engineers, and mathematicians who will focus a significant portion of their careers on national security issues.
PubMed, and Google Scholar. Invited Book Chapters .
The test datasets included scenarios for self-driving car AI, automatic object recognition in online images, and automatic detection of malware masquerading as ordinary software. "BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning". ACM, 1--18. Protecting web-based single sign-on protocols against relying party impersonation attacks through a dedicated bi-directional authenticated secure channel. Improving Model Robustness with Latent Distribution Locally and . 2014. Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner case inputs . . 2017. . Vinod Yegneswaran, SRI International. Google Scholar, my papers have been cited for over 10,000 times (h-index is 49). Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals. In Proceedings of the 36th IEEE Symposium on Security and Privacy.
[Google Scholar] Bader M. S., Loeb M., Brooks A.
Philipp Ruemmer Professor in Computer . , Adam Kortylewski, Cihang Xie, Yinzhi Cao, Alan Yuille ECCV, 2020 : Snapshot Distillation: Teacher-Student Optimization in One Generation Chenglin .
Google Scholar. Wei Meng, Chenxiong Qian, Shuang Hao, Kevin Borgolte, Giovanni Vigna, Christopher Kruegel, Wenke Lee Proceedings of the 27th USENIX Security Symposium (USENIX Security), August 2018. Texas A&M University, College Station, TX . Y Cao, Y Fratantonio, A Bianchi, M Egele, C Kruegel, G Vigna, Y Chen. 90: 2017:
Their combined citations are counted only for the first article. Research. Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner case inputs . Deepxplore: Automated whitebox testing of deep learning systems. Antiviral therapy is the primary treatment. For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).. load content from web.archive.org Neil Zhenqiang Gong, and Yinzhi Cao. 27th USENIX Security Symposium (USENIX Security 18), 711-728, 2018. These advances have led to widespread adoption and deployment of DL in security- and safety-critical .
. Submission history Cao, Xun Ho 2007 Original work published in 1986. . Yinzhi Cao, Alexander Fangxiao Yu, Andrew Aday, Eric Stahl, Jon Merwine, and Junfeng Yang. Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana. Shinji Watanabe received his Bachelor's and Master's degrees in Theoretical Physics from the Waseda University in Tokyo, Japan. Yinzhi Cao (Ph.D. 2014. In: OOPSLA (2014) Google Scholar . Yinzhi Cao, V. Yegneswaran, +1 author Yan Chen; . . 295-297, pp. X Cao, Z Li, X Song, X Cui, P Cao, H Liu, F Cheng, Y Chen. In this section, we first discuss the mathematical framework for patch-based adversarial attacks (Sect. Our experiments show that PatchAttack achieves > 99% success rate on ImageNet for a wide range of architectures, while only manipulating 3% of the image for non-targeted attacks and 10% on average for targeted attacks. . Dehong Cao, 1 , Yinzhi Shen, 2 , Yin Huang, 1 Bo Chen, 1 Zeyu Chen, 1 Jianzhong Ai, 1 Liangren Liu, 1 Lu Yang, 1 , * and Qiang Wei 1 , * . Y Cao, S Liu, Y Zhou, Y Chen, T Zhou . Efficient and Effective Feature Space Exploration for Testing Deep Learning Systems Just Accepted The following articles are merged in Scholar. Loading. Bibliographic details on BibTeX record conf/sp/CaoY15. I will host Drs. PAPERS UNDER SUBMISSION 1) JShield: Towards Real-time and Vulnerability-based Detection of Polluted Drive-by Download Attacks, Yinzhi Cao, Xiang Pan, Yan Chen and Jianwei Zhuge. Putative endothelial progenitor cells do not promote vascular repair but attenuate pericyte-myofibroblast transition in UUO-induced renal fibrosis. Invited Book Chapters 1. In Proceedings of the 26th Symposium on Operating Systems Principles.
193-197. Since Pro has shown net anti-inflammatory properties as part of its beneficial effects, we examined the potential role of Pro in the modulation of macrophage polarization status during both rI/R injury in vivo and exposure of cultured peritoneal macrophages (PMs) to .
Their combined citations are counted only for the first article. Isolation, identification, and culture of EPCs and fibroblasts. Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana. IEEE, 2015. I am a Fellow of IEEE. Google Scholar; Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. Try again later. A. Propofol (Pro) confers protection against renal ischemia/reperfusion (rI/R) injury through incompletely characterized mechanisms. arXiv preprint arXiv:1712.01785, 2017. B. Cao, M. Wang, H. Yang, K. Zhao, J. Li, et al. In SOSP. View author publications. Try again later.
Google Scholar; Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. You can also search for this author in PubMed Google . Zhaohan Xi. "Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems". I'm interested in computer vision, artificial intelligence and adversarial machine learning. . [Google Scholar] 3. Share. In Proceedings of the 30th Annual Computer Security Applications Conference (ACSAC). Yinzhi Cao. Towards Making Systems Forget with Machine Unlearning.
"Practical Blind Membership Inference Attack via Differential Comparisons". . 2017. . Their combined citations are counted only for the first article. T Zhou, S Li, Y Cao, N Gong. 2015. Local Arrangement Chair In Proceedings of the 26th Symposium on Operating Systems Principles (SOSP'17). Poultry Science 95 (9), 2160-2166, 2016. Yinzhi Cao Johns Hopkins University Verified email at jhu.edu. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs.
CY Zhou, Y Wang, JX Cao, YJ Chen, Y Liu, YY Sun, DD Pan, CR Ou. He is affiliated with the Johns Hopkins University Information Security Institute (ISI). L. Yan, S. Zhang, . Binghui Wang, Tianchen Zhou, Song Li, Yinzhi Cao, and Neil Zhenqiang Gong. Membership inference (MI) attacks affect user privacy by inferring whether given data samples have been used to train a target learning model, e.g., a deep neural network. PDF. Authors. DeepXplore: Automated Whitebox Testing of Deep Learning Systems. Such research has the goal to model, measure, and affect the quality of AI artifacts, such as data, models and applications, to then facilitate adherence to legal standards. International Workshop on Recent Advances in Intrusion Detection, 276-298. Zhu JJ, Luo J, Cao WT, Shi HP, Yao DW, et al. Yinzhi Cao, Vinod Yegneswaran, Phillip Porras and Yan Chen, "PathCutter: Severing the Self- . 2017. 15 back 2017. Paper Link to paper BibTeX. NDSS, 2015. . Article Download PDF CrossRef View Record in Scopus Google Scholar. . Jinyuan Jia, Yupei Liu, and Neil Zhenqiang Gong.
Authors: William Paul, Yinzhi Cao, Miaomiao Zhang, Phil Burlina Download PDF Abstract: Machine learning (ML) models used in medical imaging diagnostics can be vulnerable to a variety of privacy attacks, including membership inference attacks, that lead to violations of regulations governing the use of medical data and threaten to compromise .
Cross-device tracking has drawn growing attention from both commercial companies and the general public because of its privacy implications and applications for user profiling, personalized services, etc. Yinzhi Cao Johns Hopkins University Verified email at jhu.edu. 2021. BB Gupta, S Gupta, S Gangwar, M Kumar, PK Meena. Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana Due to the increasing usage of machine learning (ML) techniques in security- and safety-critical domains, such as autonomous systems and medical diagnosis, ensuring correct behavior of ML systems, especially for different corner cases, is of growing importance. 515-519. 1-18. Article Google Scholar 2021. : Phosphor: illuminating dynamic data flow in the jvm.
View 3 excerpts, references background; Save. One particular, wide-used type of cross-device tracking is to leverage browsing histories of user devices, e.g., characterized by a list of IP addresses used by the devices and domains .
- Life University Volleyball Showcase
- Tennis Background Aesthetic
- Saudi Arabia Companies Contact Details
- Ultrasound-assisted Extraction Ppt
- 1996 Honda Integra Type R Weight
- Inferno Of The Star Mounts Ruling
- Funny Nicknames For Pastors
- Long Jump World Athletics