Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous

• URL: http://arxiv.org/abs/2311.05992v1
• Date: Fri, 10 Nov 2023 11:07:31 GMT
• Title: Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous
• Authors: Ziwei Wang, Nabil Aouf, Jose Pizarro, Christophe Honvault
• Abstract summary: We propose a novel approach for adversarial attack detection for deep neural network-based relative pose estimation schemes. The proposed adversarial attack detector achieves a detection accuracy of 99.21%.
• Score: 8.191688622709444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research on developing deep learning techniques for autonomous spacecraft relative navigation challenges is continuously growing in recent years. Adopting those techniques offers enhanced performance. However, such approaches also introduce heightened apprehensions regarding the trustability and security of such deep learning methods through their susceptibility to adversarial attacks. In this work, we propose a novel approach for adversarial attack detection for deep neural network-based relative pose estimation schemes based on the explainability concept. We develop for an orbital rendezvous scenario an innovative relative pose estimation technique adopting our proposed Convolutional Neural Network (CNN), which takes an image from the chaser's onboard camera and outputs accurately the target's relative position and rotation. We perturb seamlessly the input images using adversarial attacks that are generated by the Fast Gradient Sign Method (FGSM). The adversarial attack detector is then built based on a Long Short Term Memory (LSTM) network which takes the explainability measure namely SHapley Value from the CNN-based pose estimator and flags the detection of adversarial attacks when acting. Simulation results show that the proposed adversarial attack detector achieves a detection accuracy of 99.21%. Both the deep relative pose estimator and adversarial attack detector are then tested on real data captured from our laboratory-designed setup. The experimental results from our laboratory-designed setup demonstrate that the proposed adversarial attack detector achieves an average detection accuracy of 96.29%.

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