About me

This is Ali Shahin Shmasabadi’s home on the web! I am currently a PhD intern at Inria. I am also a third-year PhD student of Centre for Intelligent Sensing (CIS) in the school of Electronic Enginnering and Computer Science (EECS) at Queen Mary University of London. Meanwhile, I am a visiting student of Imperial College London and a Enrichment student at Alan Turing Institue.

Research

I love to explore the intersection of Machine Learning, Privacy and Image Processing! My research has been published at top-tier conferences (in vision, privacy and images) such as IEEE CVPR, ACM CCS, ACM Mobisys, ICASSP, IEEE ICIP as well as IEEE transactions. These publications address the privacy risks in Machine Learning as a Service, which can be categorized in three research themes: adversarial examples for privacy protection, privacy-preserving centralized and distributed learning.

News

• [December 2020] Program Committee shadow member of 42nd IEEE Symposium on Security and Privacy, IEEE S&P 2021!
• [November 2020] A new adversarial example preprint, called FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances, Code!
• [October 2020] Giving a toturial at ACM Multimedia 2020 conference, Part2: adversarial images.
• [September 2020] Offered an internship at Inria, under supervision of Aurelien Bellet.
• [August 2020] A new adversarial preprint, called Semantically Adversarial Learnable Filters!
• [July 2020] Program Committee member of Adversarial Robustness in the Real World in ECCV 2020!
• [April 2020] Selected as 200 young researchers from all over the world for 8th HEIDELBERG LAUREATE FORUM by international experts appointed by award-granting institutions: The Association for Computing Machinery (ACM), the Norwegian Academy of Science and Letters (DNVA) and the International Mathematical Union (IMU)!
• [March 2020] Paper accepted at IEEE Transactions on Information Forensics and Security TIFS, called PrivEdge: From Local to Distributed Private Training and Prediction, Code! (impact factor 6.2)
• [March 2020] Paper accepted at IEEE Transactions on Multimedia TMM, called Exploiting Vulnerabilities of Deep Neural Networks for Privacy Protection, Code! (impact factor 5.5)
• [March 2020] Paper accepted at ACM International Conference on Mobile Systems, Applications, and Services MobiSys, called DarkneTZ: Towards Model Privacy on the Edge using Trusted Execution Environments, Code! (acceptance rate 19%)
• [Feb 2020] Paper accepted at Conference on Computer Vision and Pattern Recognition, CVPR2020, called ColorFool: Semantic Adversarial Colorization, Video, Code! (acceptance rate 22%)
• [Jan 2020] Paper accepted at 45th International Conference on Acoustics, Speech, and Signal Processing, ICASSP2020, called EDGEFOOL: AN ADVERSARIAL IMAGE ENHANCEMENT FILTER, Video, Code! (acceptance rate 19%)
• [Jan 2020] Paper accepted at IEEE Internet of Things Journal called A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics! (impact factor 9.5)
• [April 2019] Paper accepted at 26th ACM Conference on Computer and Communications Security, CCS2019, called QUOTIENT: Two-Party Secure Neural Network Training and Prediction! (acceptance rate 16%)
• [Jan 2019] Paper accepted at 44th International Conference on Acoustics, Speech, and Signal Processing, ICASSP2019, called Scene privacy protection, Code! (acceptance rate 49%)
• [June 2018] Offered a PhD Enrichment scheme, a 9-month placement at The Alan Turing Institute!
• [March 2018] Offered an internship for summer 2018 at The Alan Turing Institute, working on project Privacy-aware neural network classification & training under supervision of Adria Gascon, Matt Kusner, Varun Kanade!