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Deep hiding, embedding images with others using deep neural networks, hasdemonstrated impressive efficacy in increasing the message capacity androbustness of secret sharing. In this paper, we challenge the robustness ofexisting deep hiding schemes by preventing the recovery of secret images,building on our in-depth study of state-of-the-art deep hiding schemes andtheir vulnerabilities. Leveraging our analysis, we first propose a simplebox-free removal attack on deep hiding that does not require any priorknowledge of the deep hiding schemes.  To improve the removal performance on the deep hiding schemes that may beenhanced by adversarial training, we further design a more powerful removalattack, efficient box-free removal attack (EBRA), which employs imageinpainting techniques to remove secret images from container images. Inaddition, to ensure the effectiveness of our attack and preserve the fidelityof the processed container images, we design an erasing phase based on thelocality of deep hiding to remove secret information and then make full use ofthe visual information of container images to repair the erased visual content.Extensive evaluations show our method can completely remove secret images fromcontainer images with negligible impact on the quality of container images.

Erase and Repair: An Efficient Box-Free Removal Attack on High-Capacity Deep Hiding