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.

翻译：室内机器人导航研究的成功依赖于分割模型从RGB图像中识别自由空间的准确性。然而，深度学习模型易受对抗攻击影响，这对其实际部署构成了重大挑战。本研究揭示了神经网络隐藏层中的脆弱性，并提出了一种强化传统对抗训练的实用方法。该方法引入了一种新颖的距离损失函数，通过最小化干净图像与对抗图像在隐藏层之间的差异来实现。实验结果表明，该方法在提升模型对对抗扰动的鲁棒性方面取得了令人满意的性能。