Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors

2019-10-31 · ECCV 2020 8 · Zuxuan Wu, Ser-Nam Lim, Larry Davis, Tom Goldstein

We present a systematic study of adversarial attacks on state-of-the-art object detection frameworks. Using standard detection datasets, we train patterns that suppress the objectness scores produced by a range of commonly used detectors, and ensembles of detectors. Through extensive experiments, we benchmark the effectiveness of adversarially trained patches under both white-box and black-box settings, and quantify transferability of attacks between datasets, object classes, and detector models. Finally, we present a detailed study of physical world attacks using printed posters and wearable clothes, and rigorously quantify the performance of such attacks with different metrics.

📄 PDF Abstract BibTeX arXiv:1910.14667

Code (2)

anonymous1125/patnet_dataset

zxwu/adv_cloak pytorch

Tasks

Objectobject-detection Object Detection

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General Classification

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Adversarial Attack

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2020-05-12

We present an optimization algorithm based on a deep convolution generative adversarial network (DCGAN) to design a 2-Dimensional optical cloak. The optical cloak consists in a shell of uniform and isotropical dielectric…

Generative Adversarial Network