  In this letter, as a proof of concept, we propose a deep learning-based
approach to attack the chaos-based image encryption algorithm in
\cite{guan2005chaos}. The proposed method first projects the chaos-based
encrypted images into the low-dimensional feature space, where essential
information of plain images has been largely preserved. With the
low-dimensional features, a deconvolutional generator is utilized to regenerate
perceptually similar decrypted images to approximate the plain images in the
high-dimensional space. Compared with conventional image encryption attack
algorithms, the proposed method does not require to manually analyze and infer
keys in a time-consuming way. Instead, we directly attack the chaos-based
encryption algorithms in a key-independent manner. Moreover, the proposed
method can be trained end-to-end. Given the chaos-based encrypted images, a
well-trained decryption model is able to automatically reconstruct plain images
with high fidelity. In the experiments, we successfully attack the chaos-based
algorithm \cite{guan2005chaos} and the decrypted images are visually similar to
their ground truth plain images. Experimental results on both static-key and
dynamic-key scenarios verify the efficacy of the proposed method.
