Deep hiding, embedding images into another using deep neural networks, has
shown its great power in increasing the message capacity and robustness. In
this paper, we conduct an in-depth study of state-of-the-art deep hiding
schemes and analyze their hidden vulnerabilities. Then, according to our
observations and analysis, we propose a novel ProvablE rEmovaL attack (PEEL)
using image inpainting to remove secret images from containers without any
prior knowledge about the deep hiding scheme. We also propose a systemic
methodology to improve the efficiency and image quality of PEEL by carefully
designing a removal strategy and fully utilizing the visual information of
containers. Extensive evaluations show our attacks can completely remove secret
images and has negligible impact on the quality of containers.

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