  Adversarial attack has garnered considerable attention due to its profound
implications for the secure deployment of robots in sensitive security
scenarios. To potentially push for advances in the field, this paper studies
the adversarial attack in the black-box setting and proposes an unlabeled
data-driven adversarial attack method, called SemiAdv. Specifically, SemiAdv
achieves the following breakthroughs compared with previous works. First, by
introducing the semi-supervised learning technique into the adversarial attack,
SemiAdv substantially decreases the number of queries required for generating
adversarial samples. On average, SemiAdv only needs to query a few hundred
times to launch an effective attack with more than 90% success rate. Second,
many existing black-box adversarial attacks require massive labeled data to
mitigate the difference between the local substitute model and the remote
target model for a good attack performance. While SemiAdv relaxes this
limitation and is capable of utilizing unlabeled raw data to launch an
effective attack. Finally, our experiments show that SemiAdv saves up to 12x
query accesses for generating adversarial samples while maintaining a
competitive attack success rate compared with state-of-the-art attacks.
