Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning
This paper explores how to make robots more resilient to attacks that could trick their decision-making systems. It finds that while some methods improve their ability to resist such attacks, they often reduce the robots' overall performance.
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- 1 The supervised learning objective is to fit the function to a given dataset.
- 2 Fast attack-generating methods are typically used for computing the max in the training objective.
- 3 The TRADES algorithm optimizes a joint objective of the standard ERM term and the robustness term.
- 4 Modifications of the min-max objective have been studied in feedback systems.
Introduction
Adversarial attacks are well-studied vulnerabilities of deep neural networks. Adversarially altered inputs are barely distinguishable from the original input by humans.
Computer vision networks can be fooled by perturbations changing each pixel by a maximum of 4% while being barely noticeable by humans.
Adversarial robustness ensures consistent and predictable robot behavior in the presence of perturbations.
Research Question
The supervised learning objective is to fit the function to a given dataset. Robust learning methods aim to train robust networks.
Adversarial training changes the standard ERM objective to the min-max objective.
Fast attack-generating methods are typically used for computing the max in the training objective.
Methodology
The fast gradient sign method computes an attack. Adversarial training often uses the FGSM method due to its speed.
Study Design
The iterative fast gradient sign method is a generalization of the FGSM.
The C&W method parametrizes the attack vector to stay within a threshold.
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Results & Findings
Norm-bounded input perturbations change network decisions and impact practical robotics applications. Adversarial training improves test-time robustness at the cost of lower nominal accuracy.
- Norm-bounded input perturbations change network decisions and impact practical robotics applications.
- Adversarial training improves test-time robustness at the cost of lower nominal accuracy.
- The advanced adversarial training algorithm yielded a robust network with 89% accuracy on CIFAR-10.
- Standard training algorithms produce non-robust networks with accuracy above 96% on this dataset.
- The choice between accurate but vulnerable and robust but less accurate models is the robustness-accuracy trade-off.
A model with n parameters can fit training samples but cannot smoothly interpolate between them.
Human adversaries were studied to improve performance in robotic object manipulation tasks.
Practical Applications
The controller maps laser range scans to 7 possible categories. Certified training methods may require different hyperparameters than adversarial training.
I. Introduction
The introduction discusses the vulnerabilities of deep neural networks to adversarial attacks and the importance of adversarial robustness in robotic applications. It emphasizes the need for robots to operate reliably in diverse environments and the risks posed by adversarial manipulations.
Frequently Asked Questions
The supervised learning objective is to fit the function to a given dataset. Fast attack-generating methods are typically used for computing the max in the training objective.
The iterative fast gradient sign method is a generalization of the FGSM. The C&W method parametrizes the attack vector to stay within a threshold.
We evaluate five robust learning advancements in three robotic applications and find that combining approaches is most effective. Human adversaries were studied to improve performance in robotic object manipulation tasks.
The controller maps laser range scans to 7 possible categories. Certified training methods may require different hyperparameters than adversarial training.
A model with n parameters can fit training samples but cannot smoothly interpolate between them.
This paper explores how to make robots more resilient to attacks that could trick their decision-making systems. It finds that while some methods improve their ability to resist such attacks, they often reduce the robots’ overall performance.