  Previous works have extensively studied the transferability of adversarial
samples in untargeted black-box scenarios. However, it still remains
challenging to craft targeted adversarial examples with higher transferability
than non-targeted ones. Recent studies reveal that the traditional
Cross-Entropy (CE) loss function is insufficient to learn transferable targeted
adversarial examples due to the issue of vanishing gradient. In this work, we
provide a comprehensive investigation of the CE loss function and find that the
logit margin between the targeted and untargeted classes will quickly obtain
saturation in CE, which largely limits the transferability. Therefore, in this
paper, we devote to the goal of continually increasing the logit margin along
the optimization to deal with the saturation issue and propose two simple and
effective logit calibration methods, which are achieved by downscaling the
logits with a temperature factor and an adaptive margin, respectively. Both of
them can effectively encourage optimization to produce a larger logit margin
and lead to higher transferability. Besides, we show that minimizing the cosine
distance between the adversarial examples and the classifier weights of the
target class can further improve the transferability, which is benefited from
downscaling logits via L2-normalization. Experiments conducted on the ImageNet
dataset validate the effectiveness of the proposed methods, which outperform
the state-of-the-art methods in black-box targeted attacks. The source code is
available at \href{https://github.com/WJJLL/Target-Attack/}{Link}
