Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks
Authors: Zhiyuan Cheng, James Chenhao Liang, Guanhong Tao, Dongfang Liu, Xiangyu Zhang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of our method in white-box, black-box, and physicalworld attack scenarios, and discuss the ablations. Our code is available at https://github. com/Bob-cheng/Depth Model Hardening. Table 1: Benign performance of original and hardened models on depth estimation. |
| Researcher Affiliation | Academia | Zhiyuan Cheng Purdue University cheng443@purdue.edu James Liang Rochester Institute of Technology jcl3689@rit.edu Guanhong Tao Purdue University taog@purdue.edu Dongfang Liu Rochester Institute of Technology dongfang.liu@rit.edu Xiangyu Zhang Purdue University xyzhang@cs.purdue.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github. com/Bob-cheng/Depth Model Hardening. |
| Open Datasets | Yes | Both models are trained on the KITTI dataset (Geiger et al., 2013) and our methods fine-tune the original models publicly available. |
| Dataset Splits | Yes | We evaluate the depth estimation performance on the KITTI dataset using the Eigen split and report the results in Table 1. |
| Hardware Specification | Yes | We train our model with one GPU (Nvidia RTX A6000) that has a memory of 48G and the CPU is Intel Xeon Silver 4214R. |
| Software Dependencies | No | The paper mentions "Adam as the optimizer" but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | In adversarial training, the ranges of distance zc and viewing angle α are sampled randomly from 5 to 10 meters and -30 to 30 degrees, respectively... We generate the adversarial perturbations with two methods: L0-norm-bounded with ϵ = 1/10 and L∞-norm-bounded (i.e., PGD (Madry et al., 2018)) with ϵ = 0.1... We finetune the original model for 3 epochs on the KITTI dataset... In perturbation generation, we use 10 steps and a step size of 2.5 ϵ/10... and a batch size of 12. In MDE training, the batch size is 32, and the learning rate is 1e-5. We use Adam as the optimizer... |