7 citations · 26 across the 20 of their papers we have counts for
6 papers · 1 filter
Butterfly Effect Attack: Tiny and Seemingly Unrelated Perturbations for Object Detection
Nguyen Anh Vu Doan, Arda Yüksel, Chih-Hong Cheng
This work aims to explore and identify tiny and seemingly unrelated perturbations of images in object detection that will lead to performance degradation. While tininess can natura…
Facilitating Change Implementation for Continuous ML-Safety Assurance
Chih-Hong Cheng, Nguyen Anh Vu Doan, Balahari Balu +9
We propose a method for deploying a safety-critical machine-learning component into continuously evolving environments where an increased degree of automation in the engineering pr…
USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving
Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen +1
In this work, we consider the safety-oriented performance of 3D object detectors in autonomous driving contexts. Specifically, despite impressive results shown by the mass literatu…
Prioritizing Corners in OoD Detectors via Symbolic String Manipulation
Chih-Hong Cheng, Changshun Wu, Emmanouil Seferis +1
For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training d…
Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection
Tobias Schuster, Emmanouil Seferis, Simon Burton +1
In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations: th…
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers
Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen +1
As an emerging type of Neural Networks (NNs), Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving. In this paper, we study the robu…