arXiv:2405.05553v1 Announce Type: new Abstract: Deep learning-based lane detection (LD) plays a critical role in autonomous driving systems, such as adaptive cruise control. However, it is vulnerable to backdoor attacks. Existing backdoor attack methods on LD exhibit limited effectiveness in dynamic real-world scenarios, primarily because they fail to consider dynamic scene factors, including changes in driving perspectives (e.g., viewpoint transformations) and environmental conditions (e.g., weather or lighting changes). To tackle this issue, this paper introduces BadLANE, a dynamic scene adaptation backdoor attack for LD designed to withstand changes in real-world dynamic scene factors. To address the challenges posed by changing driving perspectives, we propose an amorphous trigger pattern composed of shapeless pixels. This trigger design allows the backdoor to be activated by various forms or shapes of mud spots or pollution on the road or lens, enabling adaptation to changes in vehicle observation viewpoints during driving. To mitigate the effects of environmental changes, we design a meta-learning framework to train meta-generators tailored to different environmental conditions. These generators produce meta-triggers that incorporate diverse environmental information, such as weather or lighting conditions, as the initialization of the trigger patterns for backdoor implantation, thus enabling adaptation to dynamic environments. Extensive experiments on various commonly used LD models in both digital and physical domains validate the effectiveness of our attacks, outperforming other baselines significantly (+25.15% on average in Attack Success Rate). Our codes will be available upon paper publication.
The article “BadLANE: A Dynamic Scene Adaptation Backdoor Attack for Lane Detection in Autonomous Driving Systems” addresses the vulnerability of deep learning-based lane detection systems to backdoor attacks. These attacks can compromise the functionality of autonomous driving systems, such as adaptive cruise control. Existing backdoor attack methods have limited effectiveness in real-world scenarios due to their failure to consider dynamic scene factors, such as changes in driving perspectives and environmental conditions. To overcome this limitation, the authors propose BadLANE, a backdoor attack that can adapt to changes in real-world dynamic scenes. They introduce an amorphous trigger pattern that can be activated by various forms or shapes of mud spots or pollution on the road or lens, allowing adaptation to changes in vehicle observation viewpoints. Additionally, they design a meta-learning framework to train meta-generators that produce meta-triggers incorporating diverse environmental information, such as weather or lighting conditions. The effectiveness of the proposed attacks is validated through extensive experiments in both digital and physical domains, outperforming other baselines significantly. The authors plan to make their codes available upon paper publication.
Exploring Dynamic Scene Adaptation in Lane Detection Backdoor Attacks
Deep learning-based lane detection (LD) is at the forefront of autonomous driving systems, enabling features like adaptive cruise control. However, this critical technology is susceptible to backdoor attacks, which can compromise the safety and reliability of autonomous vehicles. Traditional backdoor attack methods on LD have shown limited effectiveness in dynamic real-world scenarios due to the failure to consider important factors like changes in driving perspectives and environmental conditions.
In order to tackle this issue, this paper introduces an innovative approach called BadLANE, a dynamic scene adaptation backdoor attack for LD, specifically designed to withstand the challenges posed by real-world dynamic scene factors. By considering changes in driving perspectives, such as viewpoint transformations, and environmental conditions like weather or lighting changes, BadLANE offers a robust solution.
Addressing Changing Driving Perspectives
To overcome the challenges posed by changing driving perspectives, BadLANE proposes an amorphous trigger pattern composed of shapeless pixels. This unique trigger design allows the backdoor to be activated by various forms or shapes of mud spots or pollution on the road or lens. By incorporating these dynamic elements, the attack is able to adapt to changes in vehicle observation viewpoints during driving, making it more resilient and effective in real-world scenarios.
Mitigating the Effects of Environmental Changes
Environmental changes, such as weather or lighting conditions, can severely impact the performance of backdoor attacks on LD systems. To mitigate these effects, BadLANE introduces a meta-learning framework that trains meta-generators tailored to different environmental conditions. These generators produce meta-triggers that incorporate diverse environmental information as the initialization of the trigger patterns for backdoor implantation. This unique approach enables the attack to adapt and perform effectively in dynamic environments, further enhancing its robustness.
Experimental Validation
Extensive experiments were conducted on various commonly used LD models in both digital and physical domains to validate the effectiveness of BadLANE. The results showed a significant improvement in the attack success rate compared to other baselines, with an average improvement of 25.15%. These findings highlight the power of dynamic scene adaptation in backdoor attacks for LD systems and underline the importance of considering real-world factors for better defense mechanisms.
Upon paper publication, the codes for BadLANE will be made available, enabling researchers and industry professionals to further explore and develop advanced defense strategies against backdoor attacks in lane detection systems.
In conclusion, the introduction of BadLANE brings dynamic scene adaptation to the field of backdoor attacks in lane detection systems. By addressing the challenges posed by changing driving perspectives and environmental conditions, BadLANE offers a robust and effective solution. The experimental results demonstrate its superiority over existing baselines, highlighting its potential for enhancing the security and reliability of autonomous driving systems.
The paper introduces an innovative approach called BadLANE, which aims to address the vulnerability of deep learning-based lane detection (LD) systems to backdoor attacks in autonomous driving. Backdoor attacks on LD have been limited in their effectiveness, particularly in dynamic real-world scenarios, due to the failure to consider factors such as changes in driving perspectives and environmental conditions.
To overcome these limitations, BadLANE proposes an amorphous trigger pattern composed of shapeless pixels to activate the backdoor. This design allows the backdoor to be triggered by various forms or shapes of mud spots or pollution on the road or lens, enabling adaptation to changes in vehicle observation viewpoints during driving. This is a significant advancement as it allows the backdoor to function even when the perspective of the camera capturing the lane changes.
Additionally, BadLANE addresses the challenges posed by environmental changes by using a meta-learning framework. This framework trains meta-generators tailored to different environmental conditions, which produce meta-triggers incorporating diverse environmental information such as weather or lighting conditions. These meta-triggers are then used as the initialization of the trigger patterns for backdoor implantation, enabling the backdoor to adapt to dynamic environments.
The effectiveness of BadLANE is validated through extensive experiments on various commonly used LD models in both digital and physical domains. The results show that BadLANE outperforms other baselines significantly, with an average increase of 25.15% in Attack Success Rate. This demonstrates the robustness and adaptability of the proposed backdoor attack method.
Overall, this paper presents a novel and comprehensive approach to addressing backdoor attacks on LD systems in autonomous driving. By considering dynamic scene factors and incorporating amorphous trigger patterns and meta-learning techniques, BadLANE shows promising results in terms of attack success rate. Further research in this area could focus on exploring countermeasures to mitigate the impact of such attacks and enhance the security of autonomous driving systems.
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