arXiv:2407.13111v1 Announce Type: new
Abstract: Vision foundation models are increasingly employed in autonomous driving systems due to their advanced capabilities. However, these models are susceptible to adversarial attacks, posing significant risks to the reliability and safety of autonomous vehicles. Adversaries can exploit these vulnerabilities to manipulate the vehicle’s perception of its surroundings, leading to erroneous decisions and potentially catastrophic consequences. To address this challenge, we propose a novel Precision-Guided Adversarial Attack (PG-Attack) framework that combines two techniques: Precision Mask Perturbation Attack (PMP-Attack) and Deceptive Text Patch Attack (DTP-Attack). PMP-Attack precisely targets the attack region to minimize the overall perturbation while maximizing its impact on the target object’s representation in the model’s feature space. DTP-Attack introduces deceptive text patches that disrupt the model’s understanding of the scene, further enhancing the attack’s effectiveness. Our experiments demonstrate that PG-Attack successfully deceives a variety of advanced multi-modal large models, including GPT-4V, Qwen-VL, and imp-V1. Additionally, we won First-Place in the CVPR 2024 Workshop Challenge: Black-box Adversarial Attacks on Vision Foundation Models and codes are available at https://github.com/fuhaha824/PG-Attack.
Analyzing the Precision-Guided Adversarial Attack (PG-Attack) Framework
The article introduces a novel framework, called the Precision-Guided Adversarial Attack (PG-Attack), which is aimed at addressing the vulnerabilities of vision foundation models in autonomous driving systems. These models are known to be susceptible to adversarial attacks, which can lead to incorrect perception of the vehicle’s surroundings and potentially dangerous outcomes. The PG-Attack framework combines two techniques, namely Precision Mask Perturbation Attack (PMP-Attack) and Deceptive Text Patch Attack (DTP-Attack), to deceive advanced multi-modal large models.
One of the key aspects of the PG-Attack framework is its multi-disciplinary nature. It incorporates techniques from computer vision, natural language processing, and adversarial machine learning. By combining these disciplines, the framework is able to effectively manipulate the perception of autonomous vehicles, highlighting the interconnectedness of different domains in developing advanced systems.
The PMP-Attack technique is designed to precisely target the attack region while minimizing the overall perturbation. This is important as it allows the attack to be more stealthy and less likely to be detected by the model. By focusing on specific regions, the attacker can maximize the impact on the target object’s representation in the model’s feature space, leading to more convincing deceptive inputs.
The DTP-Attack introduces deceptive text patches to disrupt the model’s understanding of the scene. This technique leverages natural language processing to generate text that is strategically placed to confuse the model. By incorporating textual information into the attack, the framework enhances its effectiveness in fooling the vision foundation models.
The experiments conducted by the authors demonstrate the success of the PG-Attack framework in deceiving various advanced multi-modal large models, including GPT-4V, Qwen-VL, and imp-V1. These models are widely used in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. Therefore, the implications of these adversarial attacks are significant for the wider field.
This research highlights the need for robust defenses against adversarial attacks in autonomous driving systems. It also emphasizes the importance of considering multi-disciplinary approaches to address the vulnerabilities of complex machine learning models. The availability of the PG-Attack framework’s code on GitHub allows researchers and practitioners to study and develop countermeasures against such attacks, contributing to the overall safety and reliability of autonomous vehicles.