Biblio

Deep neural network based steganalysis has developed rapidly in recent years, which poses a challenge to the security of steganography. However, there is no steganography method that can effectively resist the neural networks for steganalysis at present. In this paper, we propose a new strategy that constructs enhanced covers against neural networks with the technique of adversarial examples. The enhanced covers and their corresponding stegos are most likely to be judged as covers by the networks. Besides, we use both deep neural network based steganalysis and high-dimensional feature classifiers to evaluate the performance of steganography and propose a new comprehensive security criterion. We also make a tradeoff between the two analysis systems and improve the comprehensive security. The effectiveness of the proposed scheme is verified with the evidence obtained from the experiments on the BOSSbase using the steganography algorithm of WOW and popular steganalyzers with rich models and three state-of-the-art neural networks.

A private information retrieval (abbreviated as PIR) protocol deals with the schemes that allow a user to retrieve privately an element of a non-replicated database. The security of PIR protocol is that the user wants to retrieve information in a database without the database knowing which information has being retrieved. This is widely applied in medical files, video or songs databases or even stock exchanges share prices. At ISIT 2008, Carlos Aguilar Melchor and Philippe Gaborit presented a lattice-based PIR protocol, whose security based on problems close to coding theory problems known to be NP-complete. In this paper, we present a practical attack on this PIR protocol when the number of elements in the database is not big. More specifically, we can firstly uncover the hidden linear relationship between the public matrices and noisy matrices, and then propose an efficient dimension-reduced attack to locate the index of the element which the user retrieved.