Since the concept of coverless information hiding was proposed, it has been greatly developed due to its effectiveness of resisting the steganographic tools. Most existing coverless image steganography (CIS) methods achieve excellent robustness under non-geometric attacks. However, they do not perform well under some geometric attacks. Towards this goal, a CIS algorithm based on DenseNet feature mapping is proposed. Deep learning is introduced to extract high-dimensional CNN features which are mapped into hash sequences. For the sender, a binary tree hash index is built to accelerate index speed of searching hidden information and DenseNet hash sequence, and then, all matched images are sent. For the receiver, the secret information can be recovered successfully by calculating the DenseNet hash sequence of the cover image. During the whole steganography process, the cover images remain unchanged. Experimental results and analysis show that the proposed scheme has better robust compared with the state-of-the-art methods under geometric attacks.

中文翻译：

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基于DenseNet特征映射的无覆盖图像隐写术

自从提出了无掩盖信息隐藏的概念以来，由于它的抗隐写工具的有效性而得到了极大的发展。大多数现有的无覆盖图像隐写术（CIS）方法在非几何攻击下均具有出色的鲁棒性。但是，它们在某些几何攻击下表现不佳。为此，提出了一种基于DenseNet特征映射的CIS算法。引入深度学习以提取映射到哈希序列中的高维CNN特征。对于发送方，构建二叉树哈希索引以加快搜索隐藏信息和DenseNet哈希序列的索引速度，然后发送所有匹配的图像。对于接收者而言，可以通过计算封面图像的DenseNet哈希序列来成功恢复秘密信息。在整个隐写术过程中，封面图像保持不变。实验结果和分析表明，与几何攻击下的最新方法相比，该方案具有更好的鲁棒性。