  The research on the single image dehazing task has been widely explored.
However, as far as we know, no comprehensive study has been conducted on the
robustness of the well-trained dehazing models. Therefore, there is no evidence
that the dehazing networks can resist malicious attacks. In this paper, we
focus on designing a group of attack methods based on first order gradient to
verify the robustness of the existing dehazing algorithms. By analyzing the
general purpose of image dehazing task, four attack methods are proposed, which
are predicted dehazed image attack, hazy layer mask attack, haze-free image
attack and haze-preserved attack. The corresponding experiments are conducted
on six datasets with different scales. Further, the defense strategy based on
adversarial training is adopted for reducing the negative effects caused by
malicious attacks. In summary, this paper defines a new challenging problem for
the image dehazing area, which can be called as adversarial attack on dehazing
networks (AADN). Code and Supplementary Material are available at
https://github.com/Xiaofeng-life/AADN Dehazing.
