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Earlence Fernandes

3 accepted papers

2021

Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect

CVPR 2021poster

Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts -- a sticker on a Stop sign, colorful borders around eyeglasses or a 3D printed object with a colorful texture. An implicit assumption here is that the perturbations must be visible so…

Cited by 90PDFcodeScholar
2021

Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems

AAAI 2021technical

Kalman Filter (KF) is widely used in various domains to perform sequential learning or variable estimation. In the context of autonomous vehicles, KF constitutes the core component of many Advanced Driver Assistance Systems (ADAS), such as Forward Collision Warning (FCW). It tracks the states (dista…

Cited by 17SourcePDFScholar
2018

Robust Physical-World Attacks on Deep Learning Visual Classification

CVPR 2018poster

Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. Given that that emerging physical systems are using DNNs in safety-critical situations, adversarial examples could mislea…

Cited by 2927SourcePDFScholar