Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Related Work
2.1 Vision-LLMs
2.2 Transferable Adversarial Attacks


Preliminaries
3.1 Revisiting Auto-Regressive Vision-LLMs
3.2 Typographic Attacks in Vision-LLMs-based AD Systems


Methodology
4.1 Auto-Generation of Typographic Attack
4.2 Augmentations of Typographic Attack
4.3 Realizations of Typographic Attacks


Experiments


Conclusion and References Related Work
2.1 Vision-LLMs
2.2 Transferable Adversarial Attacks Related Work 2.1 Vision-LLMs 2.1 Vision-LLMs 2.2 Transferable Adversarial Attacks 2.2 Transferable Adversarial Attacks Preliminaries
3.1 Revisiting Auto-Regressive Vision-LLMs
3.2 Typographic Attacks in Vision-LLMs-based AD Systems Preliminaries 3.1 Revisiting Auto-Regressive Vision-LLMs 3.1 Revisiting Auto-Regressive Vision-LLMs 3.2 Typographic Attacks in Vision-LLMs-based AD Systems 3.2 Typographic Attacks in Vision-LLMs-based AD Systems Methodology
4.1 Auto-Generation of Typographic Attack
4.2 Augmentations of Typographic Attack
4.3 Realizations of Typographic Attacks Methodology 4.1 Auto-Generation of Typographic Attack 4.1 Auto-Generation of Typographic Attack 4.2 Augmentations of Typographic Attack 4.2 Augmentations of Typographic Attack 4.3 Realizations of Typographic Attacks 4.3 Realizations of Typographic Attacks Experiments Experiments Experiments Conclusion and References Conclusion and References Conclusion and References 2.2 Transferable Adversarial Attacks Adversarial attacks are most harmful when they can be developed in a closed setting with public frameworks yet can still be realized to attack unseen, closed-source models. The literature on these transferable attacks popularly spans across gradient-based strategies. Against Vision-LLMs, our research focuses on exploring the transferability of typographic attacks. Gradient-based Attacks. Since Szegedy et al. introduced the concept of adversarial examples, gradient-based methods have become the cornerstone of adversarial attacks [23, 24]. Goodfellow et al. proposed the Fast Gradient Sign Method (FGSM [25]) to generate adversarial examples using a single gradient step, perturbing the model’s input before backpropagation. Kurakin et al. later improved FGSM with an iterative optimization method, resulting in Iterative-FGSM (I-FGSM) [26]. Projected Gradient Descent (PGD [27]) further enhances I-FGSM by incorporating random noise initialization, leading to better attack performance. Gradient-based transfer attack methods typically use a known surrogate model, leveraging its parameters and gradients to generate adversarial examples, which are then used to attack a black-box model. These methods often rely on multistep iterative optimization techniques like PGD and employ various data augmentation strategies to enhance transferability [28, 29, 30, 31, 32]. However, gradient-based methods face limitations in adversarial transferability due to the disparity between the surrogate and target models, and the tendency of adversarial examples to overfit the surrogate model [33, 34]. Gradient-based Attacks. Typographic Attacks. The development of large-scale pretrained vision-language with CLIP [11, 12] introduced a form of typographic attacks that can impair its zero-shot performances. A concurrent work [13] has also shown that such typographic attacks can extend to language reasoning tasks of Vision-LLMs like multi-choice question-answering and image-level open-vocabulary recognition. Similarly, another work [14] has developed a benchmark by utilizing a Vision-LLM to recommend an attack against itself given an image, a question, and its answer on classification datasets. Several defense mechanisms [15, 16] have been suggested by prompting the Vision-LLM to perform step-bystep reasoning. Our research differs from existing works in studying autonomous typographic attacks across question-answering scenarios of recognition, action reasoning, and scene understanding, particularly against Vision-LLMs in AD systems. Our work also discusses how they can affect reasoning capabilities at the image level, region-level understanding, and even against multiple reasoning tasks. Furthermore, we also discuss how these attacks can be realized in the physical world, particularly against AD systems. Typographic Attacks. Authors:
(1) Nhat Chung, CFAR and IHPC, A*STAR, Singapore and VNU-HCM, Vietnam;
(2) Sensen Gao, CFAR and IHPC, A*STAR, Singapore and Nankai University, China;
(3) Tuan-Anh Vu, CFAR and IHPC, A*STAR, Singapore and HKUST, HKSAR;
(4) Jie Zhang, Nanyang Technological University, Singapore;
(5) Aishan Liu, Beihang University, China;
(6) Yun Lin, Shanghai Jiao Tong University, China;
(7) Jin Song Dong, National University of Singapore, Singapore;
(8) Qing Guo, CFAR and IHPC, A*STAR, Singapore and National University of Singapore, Singapore. Authors: Authors (1) Nhat Chung, CFAR and IHPC, A*STAR, Singapore and VNU-HCM, Vietnam; (2) Sensen Gao, CFAR and IHPC, A*STAR, Singapore and Nankai University, China; (3) Tuan-Anh Vu, CFAR and IHPC, A*STAR, Singapore and HKUST, HKSAR; (4) Jie Zhang, Nanyang Technological University, Singapore; (5) Aishan Liu, Beihang University, China; (6) Yun Lin, Shanghai Jiao Tong University, China; (7) Jin Song Dong, National University of Singapore, Singapore; (8) Qing Guo, CFAR and IHPC, A*STAR, Singapore and National University of Singapore, Singapore. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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The Vulnerability of Autonomous Driving to Typographic Attacks: Transferability and Realizability

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