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Spatiotemporal Attacks for Embodied Agents

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12362)

Abstract

Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness. Existing work on adversarial attacks have mainly focused on static scenes; however, it remains unclear whether such attacks are effective against embodied agents, which could navigate and interact with a dynamic environment. In this work, we take the first step to study adversarial attacks for embodied agents. In particular, we generate spatiotemporal perturbations to form 3D adversarial examples, which exploit the interaction history in both the temporal and spatial dimensions. Regarding the temporal dimension, since agents make predictions based on historical observations, we develop a trajectory attention module to explore scene view contributions, which further help localize 3D objects appeared with highest stimuli. By conciliating with clues from the temporal dimension, along the spatial dimension, we adversarially perturb the physical properties (e.g., texture and 3D shape) of the contextual objects that appeared in the most important scene views. Extensive experiments on the EQA-v1 dataset for several embodied tasks in both the white-box and black-box settings have been conducted, which demonstrate that our perturbations have strong attack and generalization abilities (Our code can be found at https://github.com/liuaishan/SpatiotemporalAttack).

Keywords

Embodied agents Spatiotemporal perturbations 3D adversarial examples 

Notes

Acknowledgement

This work was supported by National Natural Science Foundation of China (61872021, 61690202), Beijing Nova Program of Science and Technology(Z191100001119050), Fundamental Research Funds for Central Universities (YWF-20-BJ-J-646), and ARC FL-170100117.

Supplementary material

504472_1_En_8_MOESM1_ESM.zip (14.3 mb)
Supplementary material 1 (zip 14620 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.State Key Laboratory of Software Development EnvironmentBeihang UniversityBeijingChina
  2. 2.Beijing Advanced Innovation Center for Big Data-Based Precision MedicineBeihang UniversityBeijingChina
  3. 3.UC BerkeleyLondonUSA
  4. 4.Birkbeck, University of LondonLondonUK
  5. 5.UBTECH Sydney AI Centre, School of Computer Science, Faculty of EngineeringThe University of SydneySydneyAustralia

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