  Deep neural networks (DNN) have been widely deployed in various applications.
However, many researches indicated that DNN is vulnerable to backdoor attacks.
The attacker can create a hidden backdoor in target DNN model, and trigger the
malicious behaviors by submitting specific backdoor instance. However, almost
all the existing backdoor works focused on the digital domain, while few
studies investigate the backdoor attacks in real physical world. Restricted to
a variety of physical constraints, the performance of backdoor attacks in the
real physical world will be severely degraded. In this paper, we propose a
robust physical backdoor attack method, PTB (physical transformations for
backdoors), to implement the backdoor attacks against deep learning models in
the real physical world. Specifically, in the training phase, we perform a
series of physical transformations on these injected backdoor instances at each
round of model training, so as to simulate various transformations that a
backdoor may experience in real world, thus improves its physical robustness.
Experimental results on the state-of-the-art face recognition model show that,
compared with the backdoor methods that without PTB, the proposed attack method
can significantly improve the performance of backdoor attacks in real physical
world. Under various complex physical conditions, by injecting only a very
small ratio (0.5%) of backdoor instances, the attack success rate of physical
backdoor attacks with the PTB method on VGGFace is 82%, while the attack
success rate of backdoor attacks without the proposed PTB method is lower than
11%. Meanwhile, the normal performance of the target DNN model has not been
affected.
