Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction (1904.07370v1)

Abstract: Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-driving cars. However, the security of DNN models in this context leads to major safety implications and needs to be better understood. We consider the case study of steering angle prediction from camera images, using the dataset from the 2014 Udacity challenge. We demonstrate for the first time adversarial testing-time attacks for this application for both classification and regression settings. We show that minor modifications to the camera image (an L2 distance of 0.82 for one of the considered models) result in mis-classification of an image to any class of attacker's choice. Furthermore, our regression attack results in a significant increase in Mean Square Error (MSE) by a factor of 69 in the worst case.

Security Challenges of DNNs in Self-Driving Cars: An Analysis of Evasion Attacks

The paper "Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction" provides a detailed examination of the vulnerabilities of Deep Neural Networks (DNNs) in the field of autonomous vehicle navigation, specifically focusing on steering angle prediction. This research addresses significant concerns regarding the potential for adversarial attacks to mislead neural network models, thereby posing substantial risks to the safety and efficacy of self-driving cars.

Research Context and Objectives

Autonomous vehicles represent a rapidly advancing field in contemporary technology, with Machine Learning (ML) and DNNs playing a pivotal role in their development. These vehicles rely on various sensors, including cameras and LiDAR, to assess their environment and make real-time driving decisions. However, as highlighted by the authors, the security of these ML models, particularly during testing phases, has been insufficiently examined. This paper seeks to bridge this gap by exploring the vulnerability of ML systems to evasion attacks — manipulations during the inference phase that can lead to significant deviations in model outputs.

Methodology and Experimentation

The core of this exploration involves the analysis of steering angle prediction, utilizing the dataset from the 2014 Udacity challenge. The authors have adapted two convolutional neural network (CNN) architectures — the Epoch and NVIDIA models — to simulate both classification and regression tasks. By employing the Carlini-Wagner $L_2$ attack for classification problems, and innovating a novel attack for regression tasks, the authors effectively demonstrate that minimal perturbations to camera images (e.g., an $L_2$ distance of 0.82 for the Epoch model) can cause substantial misclassification.

The paper's experimental setup is rigorous, employing cross-validation and calculating model accuracies (90% for the Epoch model and 86% for the NVIDIA model in classification tasks), thus ensuring reliability in the attack assessment. The results are revealing: adversarial attacks increase the Mean Square Error (MSE) by a factor of up to 69, notably degrading model performance.

Key Findings

1. Evasion Attack Effectiveness: The research successfully illustrates that both classification and regression models for steering predictions are susceptible to evasion attacks, with 100% attack success achievable through sophisticated perturbation techniques.
2. Model Vulnerability Differences: The NVIDIA model, with its greater complexity, requires more substantial perturbations for attack success compared to the simpler Epoch model, indicating a varying degree of resilience.
3. Increment in Prediction Error: For regression, adversarial perturbations can increase the MSE significantly, pointing to potential drastic consequences in real-world automotive applications.

Implications and Future Directions

This paper importantly sheds light on the critical need for further research into defensive strategies against adversarial attacks in autonomous vehicle systems. As the technology underlying self-driving cars continues to evolve, understanding and mitigating such vulnerabilities will be paramount. Practically, this implies a necessity for integrating robust security frameworks within ML pipelines to preclude potential adversarial disruptions that could compromise safety.

Moreover, the theoretical implications underscore the requirement for developing more resilient DNN architectures that can better withstand adversarial perturbations without sacrificing accuracy. Future work in this field may revolve around refining adversarial training methods and exploring alternative neural network configurations that inherently possess greater resistance to evasion attacks.

In conclusion, this paper offers a foundational discourse into the determinative security threats faced by DNN-driven self-driving cars, compelling the community to prioritize research investments in the intersection of cybersecurity and autonomous vehicle safety.