High-capacity image steganography, aimed at concealing a secret image in a cover image, is a technique to preserve sensitive data, e.g., faces and fingerprints. Previous methods focus on the security during transmission and subsequently run a risk of privacy leakage after the restoration of secret images at the receiving end. To address this issue, we propose a framework, called Multitask Identity-Aware Image Steganography (MIAIS), to achieve direct recognition on container images without restoring secret images. The key issue of the direct recognition is to preserve identity information of secret images into container images and make container images look similar to cover images at the same time. Thus, we introduce a simple content loss to preserve the identity information, and design a minimax optimization to deal with the contradictory aspects. We demonstrate that the robustness results can be transferred across different cover datasets. In order to be flexible for the secret image restoration in some cases, we incorporate an optional restoration network into our method, providing a multitask framework. The experiments under the multitask scenario show the effectiveness of our framework compared with other visual information hiding methods and state-of-the-art high-capacity image steganography methods.
翻译:高容量图像摄像法旨在将秘密图像隐藏在封面图像中,这是一种保存敏感数据的技术,例如脸部和指纹。以前的方法侧重于传输期间的安全,在接收端恢复秘密图像后,可能会出现隐私泄漏的风险。为解决这一问题,我们提议了一个称为“多功能身份-软件图像摄像法(MIAIS)”的框架,以便在不恢复秘密图像的情况下直接识别集装箱图像。直接识别的关键问题是将秘密图像的身份信息保存在容器图像中,使容器图像看起来相似于同时覆盖图像。因此,我们引入了简单的内容损失,以保存身份信息,并设计了处理矛盾方面的微缩最大优化。我们证明,稳健性结果可以跨越不同的覆盖数据集。为了在某些情况下对恢复秘密图像具有灵活性,我们将一个可选的恢复网络纳入我们的方法,提供一个多功能框架。多功能情景下的实验展示了我们框架与其他视觉信息隐藏方法和状态高能力图像摄像法相比的有效性。