The Vulnerability of Autonomous Driving to Typographic Attacks: Transferability and Realizability
The Vulnerability of Autonomous Driving to Typographic Attacks: Transferability and Realizability
Estimated Reading Time: ~8 minutes
- Typographic Attacks Exploit Vision-LLMs: Autonomous Driving (AD) systems are vulnerable to ‘typographic attacks’ which use subtle, text-based manipulations to mislead Vision-Language Models, potentially causing critical misinterpretations in real-world scenarios.
- Transferability Amplifies Risk: These attacks are highly transferable, meaning an attack developed for one AI model can succeed against different, unseen, proprietary models, significantly lowering the barrier for adversaries and increasing widespread risk across various AD systems.
- Realizability in the Physical World: Typographic attacks can be physically realized through alterations to signs, strategically placed stickers, or graffiti, leading to dangerous misclassifications (e.g., misinterpreting a stop sign or misreading a vehicle’s type).
- Multi-faceted Defense is Crucial: Robust AD security requires a comprehensive approach, including enhanced adversarial training, multi-modal sensor fusion for cross-verification, and continuous monitoring with anomaly detection to adapt to and mitigate new threats.
- Understanding the Threat: Typographic Attacks on Vision-LLMs in AD Systems
- The Peril of Transferability: When Attacks Go Beyond Known Models
- Realizing the Threat: From Digital Concept to Physical World Impact
- Safeguarding the Future: Actionable Steps for Robust Autonomous Driving
- Conclusion
- Frequently Asked Questions
Autonomous Driving (AD) systems promise a future of safer and more efficient transportation. At their core, these systems rely heavily on advanced artificial intelligence, particularly Vision-Language Models (Vision-LLMs), to perceive, understand, and react to their environment. From recognizing pedestrians and traffic signs to interpreting complex road conditions, the accuracy of these AI models is paramount. However, a growing concern in the field of AI security is the emergence of ‘typographic attacks’ – subtle, often text-based, manipulations designed to mislead sophisticated machine learning models.
These attacks pose a unique and potent threat to autonomous vehicles because they exploit vulnerabilities in how Vision-LLMs process visual information, potentially causing critical misinterpretations in real-world scenarios. Understanding the dual challenges of ‘transferability’ (an attack’s ability to succeed against different, unseen models) and ‘realizability’ (its capacity to be executed effectively in the physical world) is crucial for developing robust and trustworthy AD systems.
A recent comprehensive study dissecting these complex vulnerabilities, titled ‘The Vulnerability of Autonomous Driving to Typographic Attacks: Transferability and Realizability’, meticulously outlines its investigation into this critical area:
Understanding the Threat: Typographic Attacks on Vision-LLMs in AD Systems
Vision-LLMs are the perceptive eyes and brains of autonomous vehicles. They combine advanced computer vision with natural language understanding, allowing an AD system not just to ‘see’ an object but to ‘understand’ its context and implications – for instance, discerning a stop sign and comprehending its directive. This sophisticated capability, however, also presents new attack surfaces.
Typographic attacks differ fundamentally from traditional adversarial examples, which often involve imperceptible pixel-level perturbations. Instead, typographic attacks introduce visually prominent (though sometimes subtle) text, symbols, or patterns into an image that humans might easily disregard or correctly interpret, but which cause a Vision-LLM to misclassify or misunderstand a scene. For an autonomous vehicle, a misclassification could mean the difference between correctly identifying a pedestrian and seeing a non-threatening object, or misinterpreting a critical road sign.
To fully grasp the implications of these threats, it’s crucial to understand the nature of transferable adversarial attacks, particularly in contrast to traditional methods:
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].
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.
The Peril of Transferability: When Attacks Go Beyond Known Models
The concept of ‘transferability’ is arguably the most insidious aspect of adversarial attacks in the context of autonomous driving. It refers to the ability for an attack, crafted to fool one specific AI model (a ‘surrogate’ or ‘white-box’ model), to successfully compromise a different, often proprietary and inaccessible AI model (a ‘black-box’ model). In the diverse landscape of autonomous vehicles, where different manufacturers employ varied hardware, software architectures, and proprietary AI models, the transferability of an attack means that a single vulnerability could affect a broad range of vehicles, not just those from one vendor.
Unlike gradient-based methods, which often struggle with transferability due to overfitting to the surrogate model’s specific parameters, typographic attacks explore a different vector. By leveraging human-interpretable visual elements that Vision-LLMs are trained to understand, these attacks might achieve greater generalization. An attacker wouldn’t need to know the intricate workings of every AD system; they could develop an attack on a publicly available model and have a reasonable expectation of it working against a host of unseen, production-grade vehicles. This dramatically lowers the barrier for potential adversaries and amplifies the risk.
Realizing the Threat: From Digital Concept to Physical World Impact
While digital adversarial examples are concerning, their true danger to AD systems manifests when they can be ‘realized’ in the physical world. This involves translating a digital attack into a tangible manipulation that a vehicle’s sensors can perceive. For typographic attacks, this realization can take many forms: subtle alterations to existing road signs, strategically placed stickers or graffiti on infrastructure, custom license plates, or even graphics on other vehicles. The challenge lies in ensuring that these physical manifestations remain effective despite variations in lighting, viewing angles, weather conditions, and sensor noise.
The implications are profound. An autonomous vehicle could misinterpret a stop sign with a minor typographic alteration as a speed limit sign, or fail to detect a critical traffic signal. It could classify a pedestrian with a specific pattern on their clothing as a lamppost, or misread a vehicle’s type, leading to incorrect predictions of its behavior. The transition from a theoretical vulnerability to a physical threat underscores the urgent need for robust defenses.
Real-World Example: The “Ghost Vehicle” Misclassification
Imagine a scenario where a malicious actor places a seemingly innocuous, small sticker with specific typographic patterns on the back of a standard sedan. When an autonomous vehicle approaches this sedan, its Vision-LLM, instead of correctly identifying it as “car,” misclassifies it as “large commercial truck” or even “construction barrier” due to the typographic attack. This misclassification could lead the AD system to maintain an incorrect following distance, attempt an unsafe overtake, or even fail to recognize the vehicle’s braking lights properly, mistaking them for an ambient light source. Such a simple, physically realizable attack could lead to dangerous driving decisions based on a fundamentally flawed perception of the environment.
Safeguarding the Future: Actionable Steps for Robust Autonomous Driving
Addressing the dual threats of transferability and realizability requires a multi-faceted approach, combining proactive defense mechanisms and continuous vigilance. Here are three actionable steps vital for enhancing the security of autonomous driving systems:
- Enhanced Data Augmentation and Adversarial Training: Developers must move beyond conventional training data. AD systems should be extensively trained on datasets that include a wide variety of physically realized typographic attacks, under diverse environmental conditions (different lighting, weather, angles). Adversarial training, where models are exposed to adversarial examples during training, helps them learn to distinguish legitimate inputs from malicious ones, bolstering their resilience to novel attack variations.
- Multi-Modal Fusion and Cross-Verification: Reducing reliance on a single sensor or AI model is critical. Integrating and fusing data from multiple sensor types—such as cameras, LiDAR, radar, and ultrasonic sensors—allows for cross-verification. If a camera-based Vision-LLM misclassifies an object due to a typographic attack, LiDAR and radar data, which are less susceptible to visual manipulations, could provide conflicting information, flagging a potential anomaly and prompting a more cautious response.
- Continuous Monitoring and Anomaly Detection: Implement sophisticated real-time monitoring systems that continuously analyze the outputs and confidence levels of AD system components. Unusual classifications, sudden drops in confidence, or inconsistencies between different perception modules could indicate an ongoing attack. Coupled with robust over-the-air (OTA) update capabilities, this allows for rapid deployment of patches and improved models in response to newly discovered threats, creating an adaptive security posture.
Conclusion
The vulnerability of autonomous driving systems to typographic attacks, particularly concerning their transferability and physical realizability, represents a formidable challenge to the industry. These attacks exploit the very sophistication of Vision-LLMs, turning seemingly innocuous visual elements into tools for deception. As autonomous vehicles become more integrated into our daily lives, ensuring their resilience against such sophisticated threats is not merely an engineering task but a societal imperative.
By proactively investing in enhanced adversarial training, embracing multi-modal sensor fusion, and establishing continuous monitoring frameworks, we can collectively work towards building autonomous systems that are not only intelligent and efficient but also inherently secure and trustworthy, safeguarding the future of mobility.
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.
Frequently Asked Questions
- Q: What are typographic attacks in the context of autonomous driving?
- Q: Why is ‘transferability’ a significant concern for AD systems?
- Q: How can typographic attacks be ‘realized’ in the physical world?
- Q: What are the key differences between typographic attacks and traditional adversarial examples?
- Q: What actionable steps can be taken to safeguard AD systems against these attacks?
A: Typographic attacks involve subtle, often text-based, manipulations (like altered signs or patterns) introduced into an image to mislead Vision-Language Models (Vision-LLMs) used in Autonomous Driving (AD) systems. These attacks cause the AI to misclassify or misunderstand a scene, differing from traditional adversarial examples that use imperceptible pixel changes.
A: Transferability refers to an attack’s ability to fool different, unseen AI models. For AD, it means an attack crafted for one manufacturer’s system could compromise a wide range of vehicles from various vendors, significantly lowering the barrier for adversaries and amplifying the potential impact across the industry.
A: Physical realization involves translating digital attacks into tangible manipulations visible to vehicle sensors. This can include subtle alterations to existing road signs, strategically placed stickers or graffiti on infrastructure, custom license plates, or graphics on other vehicles. These physical manifestations must remain effective despite varying environmental conditions.
A: Traditional adversarial examples typically involve imperceptible, pixel-level perturbations designed to fool AI models. Typographic attacks, conversely, introduce visually prominent (though sometimes subtle) text, symbols, or patterns that humans might easily disregard or correctly interpret, but which cause Vision-LLMs to misclassify or misunderstand a scene.
A: Key steps include enhanced data augmentation and adversarial training (exposing models to attacks during training), multi-modal sensor fusion and cross-verification (using various sensors like LiDAR and radar to confirm camera data), and continuous monitoring with anomaly detection (real-time analysis for unusual classifications or inconsistencies).
