Abstract
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.
Highlights
With the proliferation of mobile terminals and the astonishing expansion of the mobile Internet, Internet of things, and cloud computing, more and more data are outsourced to cloud storage systems because once the data are shared with untrusted servers, there are potential risks that the data might be modified or replicated by unauthorized servers
We have given the antiDetection two meanings. e image is unrecognizable, such as the image’s size is not suspect; the image is indistinguishable in the image ocean. e first meaning is given because we want to keep the image noteless. e second meaning is given because we find the role of the existing statistical detection in open lossy channels is reduced. is section verifies the antiDetection’s definition from two aspects: image composition in public lossy channels and the role of statistical detection in open lossy channels
The blockchain has been widely used to verify the integrity of outsourcing data in recent years. ese methods encrypt the outsourced data into garbled code to hide the contents
Summary
With the proliferation of mobile terminals and the astonishing expansion of the mobile Internet, Internet of things, and cloud computing, more and more data are outsourced to cloud storage systems because once the data are shared with untrusted servers, there are potential risks that the data might be modified or replicated by unauthorized servers. E traditional image steganography method cannot function effectively in this lossy environment. Antistatistical detection ability is one of the essential attributes to measuring traditional steganography. Two key attributes of the measurement are message embedding capacity (payload) and antistatistical detection ability [18]. Because the existing measurement attributes do not consider the lossy environment’s application background, the evaluation of a method is incomprehensive. Ese indicate that the existing measurement attributes cannot accurately evaluate robust image steganography. (ii) Contrasting the pursuits and application background of robust image steganography with the existing measurement attributes, the attribute set PRUDA is proposed.
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