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Recent studies demonstrated that the training process of deep neural networks (DNNs) is vulnerable to backdoor attacks if third-party training resources (e.g., samples) are adopted. Specifically, the adversaries intend to embed hidden backdoors into DNNs, where the backdoor can be activated by pre-defined trigger patterns and leading malicious model predictions. My dissertation focuses on poisoning-based backdoor attacks in computer vision. Firstly, I study and propose more stealthy and effective attacks against image classification tasks in both physical and digital spaces. Secondly, I reveal the backdoor threats in visual object tracking, which is representative of critical video-related tasks. Thirdly, I explore how to exploit backdoor attacks as watermark techniques for positive purposes. I design a Python toolbox (i.e., BackdoorBox) that implements representative and advanced backdoor attacks and defenses under a unified and flexible framework, based on which to provide a comprehensive benchmark of existing methods at the end.