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This research addresses security vulnerabilities in vision-based robotic control by introducing an adaptive dynamic watermarking framework to detect replay attacks. The method integrates latent dynamics networks with reinforcement learning to balance attack detection against control performance and safety constraints, offering a novel approach for securing autonomous systems.
Dynamic Watermarking and Action-Conditioned Latent Dynamics for Secure Vision-Based Controllers
arXiv:2611.07403v1 Announce Type: new Abstract: Replay attacks are difficult to detect in vision-based control systems because previously recorded observations can remain plausible while the physical system follows a different trajectory. This paper develops an adaptive dynamic-watermarking framework for detecting replay attacks against vision-based controllers. A private Gaussian watermark is added to the nominal control input, and a control input-conditioned latent dynamics network (LDN) predicts the corresponding evolution in a compact latent space learned from image observations. Attack evidence is obtained from the discrepancy between the predicted latent response and the latent representation of the received image observation. A reinforcement learning policy adjusts the watermark covariance using the latent state and detector confidence, thereby balancing attack detectability, watermark energy, and control distortion. A Lagrangian penalty on task-specific safety costs is included during training to limit the adverse effect of the excitation. The framework is evaluated on a vision-guided UR5 manipulation task in which the robot reaches a target while avoiding a moving human arm under nominal and replay-attacked observations.
Abstract:Replay attacks are difficult to detect in vision-based control systems because previously recorded observations can remain plausible while the physical system follows a different trajectory. This paper develops an adaptive dynamic-watermarking framework for detecting replay attacks against vision-based controllers. A private Gaussian watermark is added to the nominal control input, and a control input-conditioned latent dynamics network (LDN) predicts the corresponding evolution in a compact latent space learned from image observations. Attack evidence is obtained from the discrepancy between the predicted latent response and the latent representation of the received image observation. A reinforcement learning policy adjusts the watermark covariance using the latent state and detector confidence, thereby balancing attack detectability, watermark energy, and control distortion. A Lagrangian penalty on task-specific safety costs is included during training to limit the adverse effect of the excitation. The framework is evaluated on a vision-guided UR5 manipulation task in which the robot reaches a target while avoiding a moving human arm under nominal and replay-attacked observations.
Robotics (cs.RO); Cryptography and Security (cs.CR); Systems and Control (eess.SY)
I.2.8; I.2.9; I.2.6; K.6.5
arXiv:2611.07403 [cs.RO]
(or arXiv:2611.07403v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.07403 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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