Towards Transferable Attacks Against Vision-LLMs in Autonomous Driving with Typography

• URL: http://arxiv.org/abs/2405.14169v1
• Date: Thu, 23 May 2024 04:52:02 GMT
• Title: Towards Transferable Attacks Against Vision-LLMs in Autonomous Driving with Typography
• Authors: Nhat Chung, Sensen Gao, Tuan-Anh Vu, Jie Zhang, Aishan Liu, Yun Lin, Jin Song Dong, Qing Guo,
• Abstract summary: Vision-Large-Language-Models (Vision-LLMs) are increasingly being integrated into autonomous driving (AD) systems. We propose to leverage typographic attacks against AD systems relying on the decision-making capabilities of Vision-LLMs.
• Score: 21.632703081999036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Large-Language-Models (Vision-LLMs) are increasingly being integrated into autonomous driving (AD) systems due to their advanced visual-language reasoning capabilities, targeting the perception, prediction, planning, and control mechanisms. However, Vision-LLMs have demonstrated susceptibilities against various types of adversarial attacks, which would compromise their reliability and safety. To further explore the risk in AD systems and the transferability of practical threats, we propose to leverage typographic attacks against AD systems relying on the decision-making capabilities of Vision-LLMs. Different from the few existing works developing general datasets of typographic attacks, this paper focuses on realistic traffic scenarios where these attacks can be deployed, on their potential effects on the decision-making autonomy, and on the practical ways in which these attacks can be physically presented. To achieve the above goals, we first propose a dataset-agnostic framework for automatically generating false answers that can mislead Vision-LLMs' reasoning. Then, we present a linguistic augmentation scheme that facilitates attacks at image-level and region-level reasoning, and we extend it with attack patterns against multiple reasoning tasks simultaneously. Based on these, we conduct a study on how these attacks can be realized in physical traffic scenarios. Through our empirical study, we evaluate the effectiveness, transferability, and realizability of typographic attacks in traffic scenes. Our findings demonstrate particular harmfulness of the typographic attacks against existing Vision-LLMs (e.g., LLaVA, Qwen-VL, VILA, and Imp), thereby raising community awareness of vulnerabilities when incorporating such models into AD systems. We will release our source code upon acceptance.

Related papers

• Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques [12.907116223796201]
  Vision-Language Models (VLMs) have witnessed a surge in both research and real-world applications. This work systematically investigates the impact of model design choices on the adversarial robustness of VLMs against image-based attacks.
  arXiv  Detail & Related papers  (2024-07-15T18:00:01Z)
• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• CANEDERLI: On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems [17.351539765989433]
  A growing integration of vehicles with external networks has led to a surge in attacks targeting their Controller Area Network (CAN) internal bus. As a countermeasure, various Intrusion Detection Systems (IDSs) have been suggested in the literature to prevent and mitigate these threats. Most of these systems rely on data-driven approaches such as Machine Learning (ML) and Deep Learning (DL) models. In this paper, we present CANEDERLI, a novel framework for securing CAN-based IDSs.
  arXiv  Detail & Related papers  (2024-04-06T14:54:11Z)
• Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models [18.624280305864804]
  Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP) This paper presents a comprehensive survey of the various forms of attacks targeting LLMs. We delve into topics such as adversarial attacks that aim to manipulate model outputs, data poisoning that affects model training, and privacy concerns related to training data exploitation.
  arXiv  Detail & Related papers  (2024-03-03T04:46:21Z)
• Pre-trained Trojan Attacks for Visual Recognition [106.13792185398863]
  Pre-trained vision models (PVMs) have become a dominant component due to their exceptional performance when fine-tuned for downstream tasks. We propose the Pre-trained Trojan attack, which embeds backdoors into a PVM, enabling attacks across various downstream vision tasks. We highlight the challenges posed by cross-task activation and shortcut connections in successful backdoor attacks.
  arXiv  Detail & Related papers  (2023-12-23T05:51:40Z)
• Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
  We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
  arXiv  Detail & Related papers  (2023-12-20T05:06:01Z)
• SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation [56.622250514119294]
  In contrast to white-box adversarial attacks, transfer attacks are more reflective of real-world scenarios. We propose a self-augment-based transfer attack method, termed SA-Attack.
  arXiv  Detail & Related papers  (2023-12-08T09:08:50Z)
• Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems [9.015049689314859]
  We explore learning-based attacks from the perspective of transferability. This paper categorizes and reviews the architecture of existing attacks from various viewpoints. We examine the implications of transferable attacks in practical scenarios such as autonomous driving.
  arXiv  Detail & Related papers  (2023-11-20T14:29:45Z)
• Visual Adversarial Examples Jailbreak Aligned Large Language Models [66.53468356460365]
  We show that the continuous and high-dimensional nature of the visual input makes it a weak link against adversarial attacks. We exploit visual adversarial examples to circumvent the safety guardrail of aligned LLMs with integrated vision. Our study underscores the escalating adversarial risks associated with the pursuit of multimodality.
  arXiv  Detail & Related papers  (2023-06-22T22:13:03Z)
• Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns [18.694795507945603]
  Recent studies demonstrated the vulnerability of control policies learned through deep reinforcement learning against adversarial attacks. This paper investigates the feasibility of targeted attacks through visually learned patterns placed on physical object in the environment.
  arXiv  Detail & Related papers  (2021-09-16T04:59:06Z)
• Adversarial defense for automatic speaker verification by cascaded self-supervised learning models [101.42920161993455]
  More and more malicious attackers attempt to launch adversarial attacks at automatic speaker verification (ASV) systems. We propose a standard and attack-agnostic method based on cascaded self-supervised learning models to purify the adversarial perturbations. Experimental results demonstrate that the proposed method achieves effective defense performance and can successfully counter adversarial attacks.
  arXiv  Detail & Related papers  (2021-02-14T01:56:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.