arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2201.07444cs.CRcs.AI
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用颜色隐藏数据:基于条件可逆神经网络的安全无损深度图像隐写

Hiding Data in Colors: Secure and Lossless Deep Image Steganography via Conditional Invertible Neural Networks

WuHan University(武汉大学)

Yanzhen Ren, Ting Liu, Liming Zhai, Lina Wang

摘要:

深度图像隐写是一种通过深度神经网络隐藏图像中数据的信息隐藏技术。然而,现有的深度图像隐写方法只考虑了容器图像与宿主图像的视觉相似性,而忽略了容器图像的统计安全性(隐秘性)。此外,它们通常隐藏数据限于图像类型,从而放松了无损提取的约束。在本文中,我们以统一的方式解决上述问题,并提出了深度图像隐写术,可以将任意类型的数据嵌入到图像中,以实现安全的数据隐藏和无损的数据揭示。首先,我们制定的数据隐藏作为一个图像彩色化问题,其中的数据被二值化,并进一步映射到一个灰度宿主图像的颜色信息。其次,我们设计了一个条件可逆的神经网络,它使用灰度图像作为先验知识来指导颜色的生成,并以一种安全的方式执行数据隐藏。最后,为了实现无损数据揭示,我们提出了一个多阶段的训练计划,以管理由于隐藏和揭示过程之间的舍入误差的数据丢失。大量的实验表明,该方法可以实现安全的数据隐藏,生成真实的彩色图像,并成功地抵抗隐写分析的检测。此外,我们可以在不同的场景中实现100%的揭示准确率,表明我们的隐写术在现实世界中的实际效用。

英文摘要:

Deep image steganography is a data hiding technology that conceal data in digital images via deep neural networks. However, existing deep image steganography methods only consider the visual similarity of container images to host images, and neglect the statistical security (stealthiness) of container images. Besides, they usually hides data limited to image type and thus relax the constraint of lossless extraction. In this paper, we address the above issues in a unified manner, and propose deep image steganography that can embed data with arbitrary types into images for secure data hiding and lossless data revealing. First, we formulate the data hiding as an image colorization problem, in which the data is binarized and further mapped into the color information for a gray-scale host image. Second, we design a conditional invertible neural network which uses gray-scale image as prior to guide the color generation and perform data hiding in a secure way. Finally, to achieve lossless data revealing, we present a multi-stage training scheme to manage the data loss due to rounding errors between hiding and revealing processes. Extensive experiments demonstrate that the proposed method can perform secure data hiding by generating realism color images and successfully resisting the detection of steganalysis. Moreover, we can achieve 100% revealing accuracy in different scenarios, indicating the practical utility of our steganography in the real-world.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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