Addressing Adversarial Attacks on Image Vision Systems in Autonomous Vehicles
The aim of this project is to is to develop robust defence mechanisms against adversarial attacks on image vision systems in autonomous vehicles. This involves creating and implementing algorithms that can detect and mitigate adversarial alterations in real-time, ensuring the AI systems accurately classify objects and maintain safe driving behaviours.
Research Opportunities
Summary
Adversarial attacks on image vision systems in autonomous vehicles pose a significant threat to the safety and reliability of autonomous driving technology. These attacks involve subtly altering images or sensor data to deceive the vehicle’s AI systems, leading to misclassification of objects, such as mistaking a stop sign for a 30 MPH sign. This can result in dangerous driving behaviours and potentially catastrophic accidents. The increasing threat of cyber-attacks against autonomous vehicles highlights the urgent need for robust cybersecurity measures. Manufacturers are continuously exploring ways to protect these systems, driven by both the complexity of the technology and stringent regulatory requirements. Addressing this problem is crucial to ensure the safe deployment of autonomous vehicles. By developing robust detection and mitigation algorithms, we aim to enhance the resilience of image vision systems against adversarial attacks, thereby maintaining accurate object recognition and ensuring the safety of passengers and other road users. This research is essential for building public trust in autonomous vehicles and advancing the technology towards widespread adoption.
In this project, a suite of algorithms will be developed to detect and mitigate adversarial attacks on image vision systems in autonomous vehicles. This involves creating robust, real-time detection mechanisms to identify adversarial perturbations and implementing mitigation strategies to neutralize these threats. Additionally, AI models will be trained to remain resilient under attack, ensuring accurate object classification, and integrate these solutions into existing vehicle systems for optimized, real-time processing. Extensive testing will be conducted in both simulated and real-world environments to validate reliability and safety. Building on our cyber research group’s expertise in automotive cybersecurity, developed through prior projects with industry partners like Horiba MIRA, this project brings essential support and real-world relevance to advancing the cybersecurity of autonomous vehicles.
Ideal Candidate Qualifications:
- Background Knowledge: A background in artificial intelligence and machine learning, particularly in the context of image processing and computer vision. Knowledge of cybersecurity principles, including common threats, attack vectors, and defence mechanisms.
- Technical Skills: Coding skills and along with experience in developing and implementing machine learning algorithms. The candidate should have hands-on experience with deep learning frameworks such as TensorFlow, Keras, or PyTorch, and be familiar with computer vision libraries like OpenCV. Knowledge on adversarial robustness tools, such as CleverHans, Adversarial Robustness Toolbox (ART), and Foolbox.
- Personal Attributes: Self-motivated, independent, and proactive in learning.
This project offers a unique opportunity to work at the intersection of artificial intelligence, cybersecurity, and autonomous vehicle technology.
The specific objectives of the project are:
- Detection Algorithms: To design and implement algorithms capable of detecting adversarial alterations in images and sensor data.
- Mitigation Strategies: To develop methods for mitigating the effects of detected adversarial attacks, ensuring the AI system can still function correctly.
- Robust AI Models: To enhance the robustness of AI models against adversarial attacks through training and validation.
- Real-Time Processing: To ensure all detection and mitigation processes can be performed in real-time without compromising the vehicle’s performance.
- Evaluation and Testing: To rigorously test the developed solutions in various scenarios to validate their effectiveness and reliability.
Supervisors
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Entry requirements
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Normal entry requirements are a first or upper second class honours degree from a University of the United Kingdom, or from an overseas University, or degree equivalent qualification, subject to the approval of Robert Gordon University.
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Fees & Costs
For Academic Year 2024/2025
UK/EU Students
- Full time - £4,786 per academic year
- Part time MPhil/PhD - £2,870 per academic year
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International Students
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For Academic Year 2023/2024
UK/EU Students
- Full time - £4,712 per academic year
- Part time MPhil/PhD - £2,830 per academic year
- Part time MRes/MSc by research - £2,356 per academic year
International Students
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- Part-time MPhil/PhD - £11,220 per academic year
- Part-time MRes/MSc by research - £9,350 per academic year
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Our Research degrees usually start in February and October each year. There are no formal deadlines for you to submit your application, though we recommend you submit it as early as possible in order to ensure we can help you join us at your preferred time.
Completed application forms should be accompanied by:
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- A short research proposal, in the range of 1,500 - 3,000 words, containing a rationale and background, study aims and objectives, a literature review, proposed research methods and a full reference list.
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