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.

在室内机器人导航中，我们通过分割模型在 RGB 图像中识别自由空间来提高导航精度。然而，深度学习模型容易受到对抗性攻击的影响，给其真实世界的应用带来了巨大挑战。本研究中，我们在神经网络的隐藏层中发现了漏洞，并引入了一种实用的方法来加强传统的对抗训练。我们的方法采用了一种新的距离损失函数，缩小了干净图像和对抗图像之间的隐藏层差距。实验证明该方法显著提高了模型对对抗扰动的鲁棒性。