  Backdoor attacks, representing an emerging threat to the integrity of deep
neural networks, have garnered significant attention due to their ability to
compromise deep learning systems clandestinely. While numerous backdoor attacks
occur within the digital realm, their practical implementation in real-world
prediction systems remains limited and vulnerable to disturbances in the
physical world. Consequently, this limitation has given rise to the development
of physical backdoor attacks, where trigger objects manifest as physical
entities within the real world. However, creating the requisite dataset to
train or evaluate a physical backdoor model is a daunting task, limiting the
backdoor researchers and practitioners from studying such physical attack
scenarios. This paper unleashes a recipe that empowers backdoor researchers to
effortlessly create a malicious, physical backdoor dataset based on advances in
generative modeling. Particularly, this recipe involves 3 automatic modules:
suggesting the suitable physical triggers, generating the poisoned candidate
samples (either by synthesizing new samples or editing existing clean samples),
and finally refining for the most plausible ones. As such, it effectively
mitigates the perceived complexity associated with creating a physical backdoor
dataset, transforming it from a daunting task into an attainable objective.
Extensive experiment results show that datasets created by our "recipe" enable
adversaries to achieve an impressive attack success rate on real physical world
data and exhibit similar properties compared to previous physical backdoor
attack studies. This paper offers researchers a valuable toolkit for studies of
physical backdoors, all within the confines of their laboratories.
