The goal of image steganography is to hide a full-sized image, termed secret, into another, termed cover. Prior image steganography algorithms can conceal only one secret within one cover. In this paper, we propose an adaptive local image steganography (AdaSteg) system that allows for scale- and location-adaptive image steganography. By adaptively hiding the secret on a local scale, the proposed system makes the steganography more secured, and further enables multi-secret steganography within one single cover. Specifically, this is achieved via two stages, namely the adaptive patch selection stage and secret encryption stage. Given a pair of secret and cover, first, the optimal local patch for concealment is determined adaptively by exploiting deep reinforcement learning with the proposed steganography quality function and policy network. The secret image is then converted into a patch of encrypted noises, resembling the process of generating adversarial examples, which are further encoded to a local region of the cover to realize a more secured steganography. Furthermore, we propose a novel criterion for the assessment of local steganography, and also collect a challenging dataset that is specialized for the task of image steganography, thus contributing to a standardized benchmark for the area. Experimental results demonstrate that the proposed model yields results superior to the state of the art in both security and capacity.

中文翻译：

---

寻找和隐藏：通过深度强化学习的对抗性隐写术。

图像隐写术的目标是将完整尺寸的图像（称为秘密）隐藏到另一个称为封面的图像中。先前的图像隐写算法只能在一个封面内隐藏一个秘密。在本文中，我们提出了一种自适应局部图像隐写术（AdaSteg）系统，该系统允许缩放和位置自适应图像隐写术。通过在局部范围内自适应地隐藏秘密，所提出的系统使隐写术更加安全，并进一步在单个封面内实现多秘密隐写术。具体来说，这是通过两个阶段实现的，即自适应补丁选择阶段和秘密加密阶段。给定一对秘密和掩护，首先，通过利用深度强化学习和所提出的隐写质量函数和策略网络自适应地确定用于隐藏的最佳局部补丁。然后将秘密图像转换为一块加密噪声，类似于生成对抗样本的过程，这些样本被进一步编码到封面的局部区域，以实现更安全的隐写术。此外，我们提出了一种评估局部隐写术的新标准，并收集了一个专门用于图像隐写术任务的具有挑战性的数据集，从而有助于该地区的标准化基准。实验结果表明，所提出的模型在安全性和容量方面都优于现有技术。我们提出了一种评估本地隐写术的新标准，并收集了一个专门用于图像隐写术任务的具有挑战性的数据集，从而有助于该地区的标准化基准。实验结果表明，所提出的模型在安全性和容量方面都优于现有技术。我们提出了一种评估本地隐写术的新标准，并收集了一个专门用于图像隐写术任务的具有挑战性的数据集，从而有助于该地区的标准化基准。实验结果表明，所提出的模型在安全性和容量方面都优于现有技术。