Cloud services have become an increasingly popular solution to provide different services to clients. More and more data are outsourced to the cloud for storage and computing. With this comes concern about the security of outsourced data. In recent years, homomorphic encryption, blockchain, steganography, and other technologies have been applied to the security and forensics of outsourced data. While encryption technologies such as homomorphic encryption and blockchain scramble data so that they cannot be understood, steganography hides the data so that they cannot be observed. Traditional steganography assumes that the environment is lossless. Robust steganography is grounded in traditional steganography and is proposed based on a real lossy social network environment. Thus, researchers, who study robust steganography, believe that the measurement should follow traditional steganography. However, the application scenario of robust steganography breaks through the traditional default lossless environment premise. It brings about changes in the focus of steganography algorithms. Simultaneously, the existing steganography methods miss the evaluation of applicability and ease of use. In this paper, “default parameters” are observed by comparing the process of robust image steganography with traditional image steganography. The idea of “perfecting default parameters” is proposed. Based on this, the attribute set of measuring robust image steganography is presented. We call it PRUDA (Payload, Robustness, ease of Use, antiDetection, and Applicability). PRUDA perfects default parameters observed in the process of traditional steganography algorithms. Statistics on image processing attacks in mobile social apps and analyses on existing algorithms have verified that PRUDA is reasonable and can better measure a robust steganography method in practical application scenarios.

中文翻译：

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PRUDA：针对社交网络中鲁棒隐写术的新测量属性集

云服务已成为向客户提供不同服务的日益流行的解决方案。越来越多的数据外包到云端进行存储和计算。随之而来的是对外包数据安全性的担忧。近年来，同态加密、区块链、隐写术等技术被应用于外包数据的安全取证。同态加密和区块链等加密技术将数据打乱以使其无法被理解，而隐写术则将数据隐藏起来使其无法被观察到。传统的隐写术假设环境是无损的。鲁棒隐写术以传统隐写术为基础，并基于真实的有损社交网络环境提出。因此，研究稳健隐写术的研究人员，相信测量应该遵循传统的隐写术。然而，鲁棒隐写术的应用场景突破了传统的默认无损环境前提。它带来了隐写算法重点的变化。同时，现有的隐写方法错过了适用性和易用性的评估。在本文中，通过比较鲁棒图像隐写术与传统图像隐写术的过程来观察“默认参数”。提出了“完善默认参数”的想法。在此基础上，提出了度量鲁棒图像隐写术的属性集。我们称之为 PRUDA（有效载荷、稳健性、易用性、抗检测和适用性）。PRUDA 完善了传统隐写算法过程中观察到的默认参数。