Abstract
In today’s rapidly evolving landscape of cyber-security threats and the widespread adoption of nano-scale devices, intelligent camera-based functionalities within smart cyber-physical systems (CPS) and the Internet-of-Things (IoT) encounter unprecedented challenges. These challenges stem from emerging attack vectors and security/privacy risks associated with the processing of image and video data. Beyond traditional concerns such as IP theft and data breaches, modern machine learning (ML) systems operating on visual data face significant adversarial and backdoor threats. Adversarial and backdoor attacks involve deliberate manipulations in images, exploiting vulnerabilities inherent in machine/deep learning models and learning mechanisms. These attacks can severely compromise system performance and decision-making processes. Addressing these evolving security and privacy threats necessitates continual advancements in defense and obfuscation strategies. These strategies play a crucial role in fortifying the resilience of intelligent systems deployed across diverse image and video processing applications, including computer vision. Through hands-on demonstrations and practical examples, attendees of the tutorial will gain insights into effectively defending against adversarial and backdoor attacks. These attacks target inherent vulnerabilities in ML models and learning mechanisms used for tasks such as depth estimation, object detection, and classification. Additionally, the tutorial will explore emerging threats specific to autonomous systems and mobile robots, offering strategies to safeguard these systems against evolving security and privacy risks.
Structure of the Event
In this tutorial, our goal is to create an interactive and stimulating learning environment that fosters deep understanding, active engagement, and positive learning outcomes for all participants. To achieve this, we will prioritize engagement and interaction throughout the tutorial. Interactive elements like group discussions, polls, and Q&A sessions will be seamlessly integrated to encourage active participation. Hands-on exercises and demonstrations will offer participants the chance to apply concepts in real-world scenarios, reinforcing comprehension through experiential learning. Our tutorial will follow a structured and progressive learning flow. Beginning with foundational concepts, we will gradually progress to more advanced topics. Concepts will be introduced incrementally, providing ample opportunities for reinforcement and review to ensure retention. We’ll employ a diverse range of instructional methods, including presentations, case studies, demonstrations, and group activities, to accommodate various learning preferences and styles. Multimedia elements such as videos, animations, and visual aids will be leveraged to enhance understanding and engagement. Furthermore, collaborative learning opportunities like group exercises and peer review sessions will be integrated to facilitate peer-to-peer knowledge sharing and collaboration, enriching the learning experience for all participants.
Schedule
Time | Event |
8:30 – 9:15 | Prof. Dr. Muhammed Shafique: Deep Learning: Advancements & Security Challenges |
9:15 – 9:45 | Dr. Amira Guesmi: Adversarial Attacks and Defenses |
9:45 – 10:00 | Dr. Amira Guesmi: Physical Adversarial Attacks |
10:00 – 10:30 | Coffee Break |
10:30 – 11:15 | Dr. Muhammed Abdullah Hanif: Backdoor Attacks and Defenses |
11:15 – 12:30 | Dr. Amira Guesmi and Dr. Muhammad Abdullah Hanif: Hands-on Workshops and Demos |
12:30 | Lunch |
Talk Description
Deep Learning: Advancements & Security Challenges |
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Adversarial Attacks and Defenses |
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Physical Adversarial Attacks |
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Backdoor Attacks and Defenses |
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Hands-on Workshops and Demos |
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Intended audience
The targeted audience for our tutorial includes students, researchers, practitioners, and professionals interested in the intersection of machine learning (ML) with autonomous systems and mobile robots, particularly in the context of security and privacy issues. This includes individuals with backgrounds in image and vision processing, machine learning, cybersecurity, and robotics.
Our tutorial holds significant appeal for a diverse audience due to several key factors. Firstly, we offer a comprehensive examination of the security and privacy challenges confronting ML-powered autonomous systems and mobile robots. Covering topics such as adversarial and backdoor attacks, privacy-preserving algorithms, and defenses across various image and video processing applications, our tutorial provides valuable insights into mitigating these critical threats. Secondly, with the increasing integration of ML into autonomous systems and mobile robots, addressing security and privacy concerns has become imperative. Our tutorial directly addresses these pressing issues, offering practical strategies and techniques to enhance the resilience of ML algorithms in real-world scenarios. Moreover, our commitment to fostering inclusivity and diversity ensures that participants from varied backgrounds, experiences, and perspectives are welcomed. By facilitating rich discussions and knowledge exchange among attendees with diverse expertise, we aim to promote a more comprehensive understanding of the challenges and solutions in this domain. Lastly, our tutorial offers practical insights gleaned from panel discussions, invited talks, and interactive sessions. These sessions provide actionable strategies that participants can readily apply in their research, development, and professional practice, thereby enhancing the practical relevance and applicability of the knowledge shared.