Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models

arXiv:2312.03419v3 Announce Type: replace
Abstract: 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 …

arxiv attacks attention automated backdoor backdoor attacks compromise cs.cr datasets deep learning digital digital realm emerging emerging threat framework generative generative models implementation integrity networks neural networks physical prediction real realm systems threat world