Data hiding with deep neural networks (DNNs) has experienced impressive successes in recent years. A prevailing scheme is to train an autoencoder, consisting of an encoding network to embed (or transform) secret messages in (or into) a carrier, and a decoding network to extract the hidden messages. This scheme may suffer from several limitations regarding practicability, security, and embedding capacity. In this work, we describe a different computational framework to hide images in deep probabilistic models. Specifically, we use a DNN to model the probability density of cover images, and hide a secret image in one particular location of the learned distribution. As an instantiation, we adopt a SinGAN, a pyramid of generative adversarial networks (GANs), to learn the patch distribution of one cover image. We hide the secret image by fitting a deterministic mapping from a fixed set of noise maps (generated by an embedding key) to the secret image during patch distribution learning. The stego SinGAN, behaving as the original SinGAN, is publicly communicated; only the receiver with the embedding key is able to extract the secret image. We demonstrate the feasibility of our SinGAN approach in terms of extraction accuracy and model security. Moreover, we show the flexibility of the proposed method in terms of hiding multiple images for different receivers and obfuscating the secret image.
翻译:与深层神经网络( DNNS) 隐藏的数据近年来取得了令人印象深刻的成功。 一个流行的计划是培训一个自动编码器, 包括一个编码网络, 将秘密信息嵌入( 或转换成) 承运人, 和一个解码网络, 以提取隐藏的信息。 这个计划可能因实用性、 安全性和嵌入能力等若干限制而受到影响。 在这项工作中, 我们描述一个不同的计算框架, 将图像隐藏在深度概率模型中。 具体地说, 我们用一个 DNNN 来模拟覆盖图像的概率密度, 并在所学分布的某个特定地点隐藏一个秘密图像。 作为即时化, 我们采用一个SinGAN 的编码网络, 来学习隐藏( 由嵌入钥匙生成的) 秘密图像。 我们从固定的一组噪音地图( 嵌入的钥匙) 中安装了一种确定性地图来隐藏秘密图像。 我们用SinG AN 来公开传送原始图像; 只有嵌入式钥匙的接收器能够提取一个隐秘图像的精度 。 我们用不同的方式展示了SinG 。