Adversarial attacks against embodied navigation agents have been demonstrated to significantly disrupt navigation direction and trajectory. However, existing adversarial attack methods for embodied navigation face two major challenges: (1) They primarily rely on 2D pixel perturbations within a limited set of viewpoints, struggling to mislead the agent under unseen viewing angles. (2) These methods optimize perturbations under static viewpoints, leading to attack failures in real-world scenarios with dynamic viewpoint changes. To overcome these limitations, we propose a novel adversarial attacks via 3D adversarial examples (AA3D) for embodied object navigation to realize the efficient deception of the agent in dynamic scenes. Specifically, our AA3D incorporates two core designs: radius-adaptive view modulation (RAVM) and dual-sampling optimization strategy (DSOS). RAVM adaptively modulates viewing parameters of camera to capture abundant nonstandard viewpoints, thus enhancing the attack performance of 3D adversarial objects across a wide range of viewpoints. DSOS utilizes differentiable rendering to optimize adversarial textures based on stochastic and trajectory-aware viewpoints, ensuring consistent deception of the agent during continuous and dynamically changing viewpoint transitions. Benefiting from the above designs, our AA3D persistently disrupts the navigation trajectory of the agent and misleads its decision-making in variable navigation scenarios, even under extreme viewpoints. Extensive quantitative evaluations on the Habitat-Matterport3D (HM3D) dataset demonstrate the adversarial effectiveness of our method. Compared with the baseline methods, our AA3D reduces the SR to 50 https://github.com/Delancey00/3D-attack-in-nav .
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