  Transferable adversarial attack is always in the spotlight since deep
learning models have been demonstrated to be vulnerable to adversarial samples.
However, existing physical attack methods do not pay enough attention on
transferability to unseen models, thus leading to the poor performance of
black-box attack.In this paper, we put forward a novel method of generating
physically realizable adversarial camouflage to achieve transferable attack
against detection models. More specifically, we first introduce multi-scale
attention maps based on detection models to capture features of objects with
various resolutions. Meanwhile, we adopt a sequence of composite
transformations to obtain the averaged attention maps, which could curb
model-specific noise in the attention and thus further boost transferability.
Unlike the general visualization interpretation methods where model attention
should be put on the foreground object as much as possible, we carry out attack
on separable attention from the opposite perspective, i.e. suppressing
attention of the foreground and enhancing that of the background. Consequently,
transferable adversarial camouflage could be yielded efficiently with our novel
attention-based loss function. Extensive comparison experiments verify the
superiority of our method to state-of-the-art methods.
