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2017

Note on Attacking Object Detectors with Adversarial Stickers

Eykholt, Kevin, Evtimov, Ivan, Fernandes, Earlence et al.

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Understand

Deep learning has proven to be a powerful tool for computer vision and has seen widespread adoption for numerous tasks.

However, deep learning algorithms are known to be vulnerable to adversarial examples.
These adversarial inputs are created such that, when provided to a deep learning algorithm, they are very likely to be mislabeled.
This can be problematic when deep learning is used to assist in safety critical decisions.

Note on Attacking Object Detectors with Adversarial Stickers · Around

Built on

Explaining and harnessing adversarial examples
Original
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Original
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
Earlier work this paper cites.
Adversarial manipulation of deep representations
Original
S. Sabour, Y. Cao, F. Faghri, and D. J. Fleet · 2015
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Then

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