  Adversarial attacks have been alerting the artificial intelligence community
recently, since many machine learning algorithms were found vulnerable to
malicious attacks. This paper studies adversarial attacks to scale-free
networks to test their robustness in terms of statistical measures. In addition
to the well-known random link rewiring (RLR) attack, two heuristic attacks are
formulated and simulated: degree-addition-based link rewiring (DALR) and
degree-interval-based link rewiring (DILR). These three strategies are applied
to attack a number of strong scale-free networks of various sizes generated
from the Barab\'asi-Albert model. It is found that both DALR and DILR are more
effective than RLR, in the sense that rewiring a smaller number of links can
succeed in the same attack. However, DILR is as concealed as RLR in the sense
that they both are constructed by introducing a relatively small number of
changes on several typical structural properties such as average shortest
path-length, average clustering coefficient, and average diagonal distance. The
results of this paper suggest that to classify a network to be scale-free has
to be very careful from the viewpoint of adversarial attack effects.
