Recently, there has been a notable surge in the development of generative models dedicated to synthesizing 3D scenes. In these research works, Neural Radiance Fields(NeRF) is one of the most popular AI approaches due to its outstanding performance with relatively smaller model size and fast training/ rendering time. Owing to its popularity, it is important to investigate the NeRF model security concern. If it is widely used for different applications with some fatal security issues would cause some serious problems. Meanwhile, as for AI security and model robustness research, an emerging adversarial Bit Flip Attack (BFA) is demonstrated to be able to greatly reduce AI model accuracy by flipping several bits out of millions of weight parameters stored in the computer's main memory. Such malicious fault injection attack brings emerging model robustness concern for the widely used NeRF-based 3D modeling. This master thesis is targeting to study the NeRF model robustness against the adversarial bit flip attack. Based on the research works the fact can be discovered that the NeRF model is highly vulnerable to BFA, where the rendered image quality will have great degradation with only several bit flips in the model parameters.

Over the past few years, the Internet of Things (IoT) has become an essential element of daily life. At the core of IoT are the densely deployed heterogeneous IoT sensors, such as RFID tags, cameras, temperature sensors, pressure sensors. These sensors work collectively to sense and capture intricate details of the surroundings, enabling the provision of highly detailed and comprehensive information. This fine-grained information encompasses a wide range of critical parameters that contribute to intelligent decision-making processes. Therefore, the security and privacy of heterogeneous IoT systems are indispensable. The heterogeneous nature of IoT systems poses a number of security and privacy challenges, including device security and privacy, data security and privacy, communication security, and AI and machine learning security. This dissertation delves into specific research issues related to device, communication, and data security, addressing them comprehensively. By focusing on these critical aspects, this work aims to enhance the security and privacy of heterogeneous IoT systems, contributing to their reliable and trustworthy operation. Specifically, Chapter 1 introduces the challenges and existing solutions in heterogeneous IoT systems. Chapter 2 presents SmartRFID, a novel UHF RFID authentication system to promote commodity crypto-less UHF RFID tags for security-sensitive applications. Chapter 3 presents WearRF-CLA, a novel CLA scheme built upon increasingly popular wrist wearables and UHF RFID systems. Chapter 4 presents the design and evaluation of PhyAuth, a PHY message authentication framework against packet-inject attacks in ZigBee networks. Chapter 5 presents NeighborWatch, a novel image-forgery detection framework for multi-cameras system with OFoV. Chapter 6 discusses the future work.

The field of computer vision has achieved tremendous progress over recent years with innovations in deep learning and neural networks. The advances have unprecedentedly enabled an intelligent agent to understand the world from its visual observations, such as recognizing an object, detecting the object's position, and estimating the distance to the object. It then comes to a question of how such visual understanding can be used to support the agent's decisions over its actions to perform a task. This dissertation aims to study this question in which several methods are presented to address the challenges in learning a desirable action policy from the agent's visual inputs for the agent to perform a task well. Specifically, this dissertation starts with learning an action policy from high dimensional visual observations by improving the sample efficiency. The improved sample efficiency is achieved through a denser reward function defined upon the visual understanding of the task, and an efficient exploration strategy equipped with a hierarchical policy. It further studies the generalizable action policy learning problem. The generalizability is achieved for both a fully observable task with local environment dynamic captured by visual representations, and a partially observable task with global environment dynamic captured by a novel graph representation. Finally, this dissertation explores learning from human-provided priors, such as natural language instructions and demonstration videos for better generalization ability.