With the fast development of machine learning technologies, deep learning
models have been deployed in almost every aspect of everyday life. However, the
privacy and security of these models are threatened by adversarial attacks.
Among which black-box attack is closer to reality, where limited knowledge can
be acquired from the model. In this paper, we provided basic background
knowledge about adversarial attack and analyzed four black-box attack
algorithms: Bandits, NES, Square Attack and ZOsignSGD comprehensively. We also
explored the newly proposed Square Attack method with respect to square size,
hoping to improve its query efficiency.