Enhancing Robustness of Indoor Robotic Navigation with Free-Space
  Segmentation Models Against Adversarial Attacks
Qiyuan An Christos Sevastopoulos Fillia Makedon
Endeavors in indoor robotic navigation rely on the accuracy of segmentation
models to identify free space in RGB images. However, deep learning models are
vulnerable to adversarial attacks, posing a significant challenge to their
real-world deployment. In this study, we identify vulnerabilities within the
hidden layers of neural networks and introduce a practical approach to
reinforce traditional adversarial training. Our method incorporates a novel
distance loss function, minimizing the gap between hidden layers in clean and
adversarial images. Experiments demonstrate satisfactory performance in
improving the model's robustness against adversarial perturbations.