Robust Adversarial Attacks Detection for Deep Learning based Relative
  Pose Estimation for Space Rendezvous
Ziwei Wang Nabil Aouf Jose Pizarro Christophe Honvault
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%.