no code implementations • 31 Jul 2022 • Mohammad Hossein Samavatian, Saikat Majumdar, Kristin Barber, Radu Teodorescu
DNNs are known to be vulnerable to so-called adversarial attacks that manipulate inputs to cause incorrect results that can be beneficial to an attacker or damaging to the victim.
no code implementations • 20 Jul 2021 • Saikat Majumdar, Mohammad Hossein Samavatian, Kristin Barber, Radu Teodorescu
These attacks make small imperceptible modifications to inputs that are sufficient to induce the DNNs to produce the wrong classification.
no code implementations • 9 Jun 2021 • Mohammad Hossein Samavatian, Saikat Majumdar, Kristin Barber, Radu Teodorescu
This paper presents HASI, a hardware-accelerated defense that uses a process we call stochastic inference to detect adversarial inputs.
11 code implementations • 19 Sep 2019 • Sobhan Moosavi, Mohammad Hossein Samavatian, Srinivasan Parthasarathy, Radu Teodorescu, Rajiv Ramnath
Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.
no code implementations • 7 Nov 2018 • Mohammad Hossein Samavatian, Anys Bacha, Li Zhou, Radu Teodorescu
At the same time, the sequential nature of input/weight processing of RNNs mitigates one of the downsides of DWM, which is the linear (rather than constant) data access time. RNNFast is very efficient and highly scalable, with flexible mapping of logical neurons to RNN hardware blocks.