As AI adoption continues to grow, it’s clear we need industry security standards so it is safe for everyone. At @Google we’re excited to introduce our Secure AI Framework (SAIF) designed to raise the security bar and reduce risk, together as an industry
https://t.co/313B3uy4tc
The deadline for IEEE Network, Special Issue: Advanced Networking and Applications for Metaverse and Web 3.0 has been extended to June 25. Please consider to submit your paper. Your contributions are highly appreciated!
https://t.co/exXKhP6mOn
Brian Yuan (CS) is the PI of a $500K National Science Foundation grant titled, “CNS Core: Small: Privacy-Preserving On-Device Intelligence in the IoT Era.” Congratulations, Brian! https://t.co/60Edr1TbIn
@michigantech@NSF#michigantech#computing#computerscience
New Research Topic in @FrontSignalPro - showcasing the work of Early Career Researchers across the entire breadth of #ImageProcessing
Led by Shiba Kuanar (@ShibaPrasadUTA), Xiaoyong Yuan (@chbrian77) & Pan He
Read more here > https://t.co/Cz2lkyC7e5
Super excited to share our paper "Membership Inference Attacks and Defenses in Neural Network Pruning" accepted at @USENIXSecurity 2022.
Extended version: https://t.co/R3mW2n7FVa
Code: https://t.co/Oz0QDVVq3w
We observe that neural network pruning disproportionately affects the pruned model's behavior for members and non-members. The increased privacy risk of pruned models is demonstrated based on our proposed membership inference attack.
We introduced Propedeutica, a framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning and deep learning techniques. Thanks, @gracesrm@abedgregio @dseabraoliveira @andyli_uf
I am happy to share that our paper "Learning Fast and Slow: Propedeutica for Real-Time Malware Detection" has been published in IEEE Transactions on Neural Networks and Learning Systems (TNNLS). https://t.co/U0B8PPVQGI