Consistent Attack: Universal Adversarial Perturbation on Embodied Vision Navigation. (arXiv:2206.05751v2 [cs.LG] UPDATED)

Embodied agents in vision navigation coupled with deep neural networks have
attracted increasing attention. However, deep neural networks are vulnerable to
malicious adversarial noises, which may potentially cause catastrophic failures
in Embodied Vision Navigation. Among these adversarial noises, universal
adversarial perturbations (UAP), i.e., the image-agnostic perturbation applied
on each frame received by the agent, are more critical for Embodied Vision
Navigation since they are computation-efficient and application-practical
during the attack. However, existing UAP methods do not consider the system
dynamics …

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