1 code implementation • 12 Apr 2024 • Dipkamal Bhusal, Md Tanvirul Alam, Monish K. Veerabhadran, Michael Clifford, Sara Rampazzi, Nidhi Rastogi
However, we observe that both model predictions and feature attributions for input samples are sensitive to noise.
no code implementations • 7 Jan 2024 • Takami Sato, Sri Hrushikesh Varma Bhupathiraju, Michael Clifford, Takeshi Sugawara, Qi Alfred Chen, Sara Rampazzi
We evaluate the effectiveness of the ILR attack with real-world experiments against two major traffic sign recognition architectures on four IR-sensitive cameras.
no code implementations • 24 Jan 2023 • Yan Long, Pirouz Naghavi, Blas Kojusner, Kevin Butler, Sara Rampazzi, Kevin Fu
Our paper characterizes the limits of acoustic information leakage caused by structure-borne sound that perturbs the POV of smartphone cameras.
no code implementations • 31 Oct 2022 • Dipkamal Bhusal, Rosalyn Shin, Ajay Ashok Shewale, Monish Kumar Manikya Veerabhadran, Michael Clifford, Sara Rampazzi, Nidhi Rastogi
Interpretability, trustworthiness, and usability are key considerations in high-stake security applications, especially when utilizing deep learning models.
no code implementations • 1 Mar 2022 • Nidhi Rastogi, Sara Rampazzi, Michael Clifford, Miriam Heller, Matthew Bishop, Karl Levitt
We present a model that explains \textit{certainty} and \textit{uncertainty} in sensor input -- a missing characteristic in data collection.
no code implementations • 16 Jul 2019 • Yulong Cao, Chaowei Xiao, Benjamin Cyr, Yimeng Zhou, Won Park, Sara Rampazzi, Qi Alfred Chen, Kevin Fu, Z. Morley Mao
In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored.