Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving

TL;DR

This paper systematically evaluates the zero-shot adversarial robustness of the Segment-Anything Model (SAM) in autonomous driving, demonstrating its robustness without additional training and discussing implications for trustworthy AI.

Contribution

It provides the first comprehensive empirical study of SAM's zero-shot adversarial robustness in autonomous driving scenarios, highlighting its potential for safe AI applications.

Findings

SAM shows acceptable robustness under black-box corruptions.

SAM maintains robustness against white-box adversarial attacks.

Large model size and training data contribute to emergent adversarial robustness.

Abstract

Semantic segmentation is a significant perception task in autonomous driving. It suffers from the risks of adversarial examples. In the past few years, deep learning has gradually transitioned from convolutional neural network (CNN) models with a relatively small number of parameters to foundation models with a huge number of parameters. The segment-anything model (SAM) is a generalized image segmentation framework that is capable of handling various types of images and is able to recognize and segment arbitrary objects in an image without the need to train on a specific object. It is a unified model that can handle diverse downstream tasks, including semantic segmentation, object detection, and tracking. In the task of semantic segmentation for autonomous driving, it is significant to study the zero-shot adversarial robustness of SAM. Therefore, we deliver a systematic empirical study on the robustness of SAM without additional training. Based on the experimental results, the zero-shot adversarial robustness of the SAM under the black-box corruptions and white-box adversarial attacks is acceptable, even without the need for additional training. The finding of this study is insightful in that the gigantic model parameters and huge amounts of training data lead to the phenomenon of emergence, which builds a guarantee of adversarial robustness. SAM is a vision foundation model that can be regarded as an early prototype of an artificial general intelligence (AGI) pipeline. In such a pipeline, a unified model can handle diverse tasks. Therefore, this research not only inspects the impact of vision foundation models on safe autonomous driving but also provides a perspective on developing trustworthy AGI. The code is available at: https://github.com/momo1986/robust_sam_iv.