Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models. (arXiv:2312.03419v2 [cs.CR] UPDATED)

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 …

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