TranSegPGD: Improving Transferability of Adversarial Examples on
  Semantic Segmentation
Xiaojun Jia Jindong Gu Yihao Huang Simeng Qin Qing Guo Yang Liu Xiaochun Cao
Transferability of adversarial examples on image classification has been
systematically explored, which generates adversarial examples in black-box
mode. However, the transferability of adversarial examples on semantic
segmentation has been largely overlooked. In this paper, we propose an
effective two-stage adversarial attack strategy to improve the transferability
of adversarial examples on semantic segmentation, dubbed TranSegPGD.
Specifically, at the first stage, every pixel in an input image is divided into
different branches based on its adversarial property. Different branches are
assigned different weights for optimization to improve the adversarial
performance of all pixels.We assign high weights to the loss of the
hard-to-attack pixels to misclassify all pixels. At the second stage, the
pixels are divided into different branches based on their transferable property
which is dependent on Kullback-Leibler divergence. Different branches are
assigned different weights for optimization to improve the transferability of
the adversarial examples. We assign high weights to the loss of the
high-transferability pixels to improve the transferability of adversarial
examples. Extensive experiments with various segmentation models are conducted
on PASCAL VOC 2012 and Cityscapes datasets to demonstrate the effectiveness of
the proposed method. The proposed adversarial attack method can achieve
state-of-the-art performance.