Abstract Deep learning has achieved best-in-class performance in many application domains and has been widely used in different scenarios such as self-driving cars, healthcare, and robotics. However, deep neural networks are also vulnerable under adversarial attacks. This talk will introduce new methods for generating adversarial attacks leveraging ADMM (alternating direction method of multipliers) and experimentations in designing adversarial examples in the physical world. The designed physical world adversarial T-shirt to evade neural network detection has been broadly featured and cited in over 100 media outlets including Communications of the ACM, The Register, Boston Globe, New Yorkers, to name a few. Besides advanced attack methods, this talk will discuss a concurrent adversarial training and model compression technique, which can achieve simultaneous model robustness and compactness for the deep learning applications in security-critical and resource-limited computing environment. The second part of the talk will introduce our hardware-aware deep neural network weight pruning method targeting the FPGA platforms and 3D convolutional neural networks for video recognition. Furthermore, the talk will discuss our new privacy-preserving weight pruning techniques.

Bio: Dr. Xue (Shelley) Lin is an assistant professor in the Department of Electrical and Computer Engineering at Northeastern University since 2017. She received her bachelor’s degree in Microelectronics from Tsinghua University, China and her PhD degree from the Department of Electrical Engineering at University of Southern California in 2016. Her research interests include deep learning security and hardware acceleration, machine learning and computing in cyber-physical systems, high-performance and mobile cloud computing systems, and VLSI. Her research work has been recognized by several NSF awards and supported by Air Force Research Lab, Office of Naval Research, and Lawrence Livermore National Lab. She got the best paper award at ISVLSI 2014 and the top paper award at CLOUD 2014.