  Trojan attack on deep neural networks, also known as backdoor attack, is a
typical threat to artificial intelligence. A trojaned neural network behaves
normally with clean inputs. However, if the input contains a particular
trigger, the trojaned model will have attacker-chosen abnormal behavior.
Although many backdoor detection methods exist, most of them assume that the
defender has access to a set of clean validation samples or samples with the
trigger, which may not hold in some crucial real-world cases, e.g., the case
where the defender is the maintainer of model-sharing platforms. Thus, in this
paper, we propose FreeEagle, the first data-free backdoor detection method that
can effectively detect complex backdoor attacks on deep neural networks,
without relying on the access to any clean samples or samples with the trigger.
The evaluation results on diverse datasets and model architectures show that
FreeEagle is effective against various complex backdoor attacks, even
outperforming some state-of-the-art non-data-free backdoor detection methods.
