  Backdoor attacks have emerged as a primary threat to (pre-)training and
deployment of deep neural networks (DNNs). While backdoor attacks have been
extensively studied in a body of works, most of them were focused on
single-trigger attacks that poison a dataset using a single type of trigger.
Arguably, real-world backdoor attacks can be much more complex, e.g., the
existence of multiple adversaries for the same dataset if it is of high value.
In this work, we investigate the practical threat of backdoor attacks under the
setting of \textbf{multi-trigger attacks} where multiple adversaries leverage
different types of triggers to poison the same dataset. By proposing and
investigating three types of multi-trigger attacks, including parallel,
sequential, and hybrid attacks, we provide a set of important understandings of
the coexisting, overwriting, and cross-activating effects between different
triggers on the same dataset. Moreover, we show that single-trigger attacks
tend to cause overly optimistic views of the security of current defense
techniques, as all examined defense methods struggle to defend against
multi-trigger attacks. Finally, we create a multi-trigger backdoor poisoning
dataset to help future evaluation of backdoor attacks and defenses. Although
our work is purely empirical, we hope it can help steer backdoor research
toward more realistic settings.
