  Deep neural networks are vulnerable to adversarial examples, and adversarial
attacks that generate adversarial examples have been studied in this context.
Existing studies imply that increasing the diversity of model outputs
contributes to improving the attack performance. This study focuses on the Auto
Conjugate Gradient (ACG) attack, which is inspired by the conjugate gradient
method and has a high diversification performance. We hypothesized that
increasing the distance between two consecutive search points would enhance the
output diversity. To test our hypothesis, we propose Rescaling-ACG (ReACG),
which automatically modifies the two components that significantly affect the
distance between two consecutive search points, including the search direction
and step size. ReACG showed higher attack performance than that of ACG, and is
particularly effective for ImageNet models with several classification classes.
Experimental results show that the distance between two consecutive search
points enhances the output diversity and may help develop new potent attacks.
The code is available at \url{https://github.com/yamamura-k/ReACG}
