AMENDATORY DEFINITION OF SECURITY FOR STEGANOGRAPHIC SYSTEM

The security of steganographic system is significant in this research field. The security defined by relative entropy D (P C || PS )is widely cited to measure the security of different steg-anographic systems. However,two examples are presented to show that some limitations exist in Cachin’s definition. Based on the analysis of a basal hypothesis testing problem,a very useful con-clusion can be drawn to define the security of steganographic system. According to the above illation,an amendatory definition is presented based on the probability of the empirical probability distribution. With the help of new definition,the relationship between security and capacity can be interpreted clearly,and truly secure steganographic method could be designed.

Coverless Steganography for Digital Images Based on a Generative Model

In this paper,we propose a novel coverless image steganographic scheme based on a generative model.In our scheme,the secret image is first fed to the generative model database,to generate a meaning-normal and independent image different from the secret image.The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image.Thus,we only need to transmit the meaning-normal image which is not related to the secret image,and we can achieve the same effect as the transmission of the secret image.This is the first time to propose the coverless image information steganographic scheme based on generative model,compared with the traditional image steganography.The transmitted image is not embedded with any information of the secret image in this method,therefore,can effectively resist steganalysis tools.Experimental results show that our scheme has high capacity,security and reliability.

Digital Image Steganographer Identification:A Comprehensive Survey

The rapid development of the internet and digital media has provided convenience while also posing a potential risk of steganography abuse.Identifying steganographer is essential in tracing secret information origins and preventing illicit covert communication online.Accurately discerning a steganographer from many normal users is challenging due to various factors,such as the complexity in obtaining the steganography algorithm,extracting highly separability features,and modeling the cover data.After extensive exploration,several methods have been proposed for steganographer identification.This paper presents a survey of existing studies.Firstly,we provide a concise introduction to the research background and outline the issue of steganographer identification.Secondly,we present fundamental concepts and techniques that establish a general framework for identifying steganographers.Within this framework,state-of-the-art methods are summarized from five key aspects:data acquisition,feature extraction,feature optimization,identification paradigm,and performance evaluation.Furthermore,theoretical and experimental analyses examine the advantages and limitations of these existing methods.Finally,the survey highlights outstanding issues in image steganographer identification that deserve further research.

A Method to Estimate the Steganographic Capacity in DCT Domain Based on MCUU Model

In order to estimate maximum steganographic capacity of discrete cosine transform（DCT） domain in JPEG image, this paper presents a method based on the maximize capacity under undetectable model（MCUU）. We analyze the relation between steganographic capacity and affecting factors（image size, steganography operator, loading band, embedding intensity and image complexity）. Then we design a steganography analyzer architecture and a steganographic algorithm which can dynamically increase the steganographic capacity. Compared with other methods of embedding capacity estimation in DCT domain, the proposed methods utilizes general steganalysis methods rather than one specific steganalysis technique and takes five essential factors into account, which improves the commonality and comprehensiveness of capacity estimation, respectively. The experimental results show that steganographic capacity for quantization index modulation（QIM） is almost twice that of spread spectrum（SS） based on MCUU model.