Deep neural networks (DNNs) are demonstrated to be vulnerable to universal perturbation, a single quasi-perceptible perturbation that can deceive the DNN on most images. However, the previous works are focused on using universal perturbation to perform adversarial attacks, while the potential usability of universal perturbation as data carriers in data hiding is less explored, especially for the key-controlled data hiding method. In this paper, we propose a novel universal perturbation-based secret key-controlled data-hiding method, realizing data hiding with a single universal perturbation and data decoding with the secret key-controlled decoder. Specifically, we optimize a single universal perturbation, which serves as a data carrier that can hide multiple secret images and be added to most cover images. Then, we devise a secret key-controlled decoder to extract different secret images from the single container image constructed by the universal perturbation by using different secret keys. Moreover, a suppress loss function is proposed to prevent the secret image from leakage. Furthermore, we adopt a robust module to boost the decoder's capability against corruption. Finally, A co-joint optimization strategy is proposed to find the optimal universal perturbation and decoder. Extensive experiments are conducted on different datasets to demonstrate the effectiveness of the proposed method. Additionally, the physical test performed on platforms (e.g., WeChat and Twitter) verifies the usability of the proposed method in practice.

翻译：深度神经网络（DNN）已被证明易受通用扰动影响——即一种能欺骗DNN对大部分图像做出错误判断的准可感知扰动。然而，先前研究主要集中在利用通用扰动进行对抗性攻击，而将其作为数据隐藏载体（尤其是密钥控制型数据隐藏方法）的潜在用途尚未得到充分探索。本文提出一种基于通用扰动的新型密钥控制数据隐藏方法，通过单一通用扰动实现数据隐藏，并借助密钥控制解码器进行数据提取。具体而言，我们优化一个通用扰动作为数据载体，该扰动可隐藏多幅秘密图像并能添加到多数封面图像中。随后设计密钥控制解码器，通过使用不同密钥从通用扰动的单个容器图像中提取不同秘密图像。此外，提出抑制损失函数以防止秘密图像泄露，并引入鲁棒模块增强解码器对抗图像扰动的能力。最终提出联合优化策略以寻找最优通用扰动与解码器。在不同数据集上的大量实验证明了该方法的有效性，同时在社交平台（如微信和推特）上开展的物理测试验证了该方法在实际应用中的可行性。