Enhancing Robustness of Indoor Robotic Navigation with Free-Space Segmentation Models Against Adversarial Attacks
<|reference_start|>Enhancing Robustness of Indoor Robotic Navigation with Free-Space Segmentation Models Against Adversarial Attacks: 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.<|reference_end|>
@article{an2024enhancing,
    title={Enhancing Robustness of Indoor Robotic Navigation with Free-Space
  Segmentation Models Against Adversarial Attacks},
    author={Qiyuan An, Christos Sevastopoulos, Fillia Makedon},
    journal={arXiv preprint arXiv:2402.08763},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08763},
    primaryClass={cs.CV}
}