Deep hiding, embedding images with others using deep neural networks, has demonstrated impressive efficacy in increasing the message capacity and robustness of secret sharing. In this paper, we challenge the robustness of existing deep hiding schemes by preventing the recovery of secret images, building on our in-depth study of state-of-the-art deep hiding schemes and their vulnerabilities. Leveraging our analysis, we first propose a simple box-free removal attack on deep hiding that does not require any prior knowledge of the deep hiding schemes. To improve the removal performance on the deep hiding schemes that may be enhanced by adversarial training, we further design a more powerful removal attack, efficient box-free removal attack (EBRA), which employs image inpainting techniques to remove secret images from container images. In addition, to ensure the effectiveness of our attack and preserve the fidelity of the processed container images, we design an erasing phase based on the locality of deep hiding to remove secret information and then make full use of the visual information of container images to repair the erased visual content. Extensive evaluations show our method can completely remove secret images from container images with negligible impact on the quality of container images.

翻译：深度隐藏技术利用深度神经网络将图像嵌入其他图像中，在提升秘密共享的消息容量和鲁棒性方面展现出显著效果。本文基于对当前最先进的深度隐藏方案及其漏洞的深入研究，通过阻止秘密图像的恢复来挑战现有深度隐藏方案的鲁棒性。基于分析结果，我们首先提出一种简单的无框移除攻击方法，无需任何关于深度隐藏方案的先验知识即可实施。针对可能通过对抗训练增强的深度隐藏方案，我们进一步设计了一种更强大的移除攻击——高效无框移除攻击（EBRA），该攻击采用图像修复技术从容器图像中移除秘密图像。此外，为确保攻击有效性并保持处理后容器图像的保真度，我们基于深度隐藏的局部性设计了擦除阶段以移除秘密信息，随后充分利用容器图像的视觉信息修复擦除后的视觉内容。大量评估表明，所提方法能在对容器图像质量影响极小的情况下，完全移除其中的秘密图像。