Xiong Qi

Bio: Xiong Qi is an academic researcher. The author has contributed to research in topics: Steganalysis & Steganography. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Research on the Large Scale Image Steganalysis Technology Based on Cloud Computing and BP Neutral Network

Abstract: Steganalysis is the opposite art to steganography, whose goal is to detect whether or not the seemly innocent objects like image hiding message. With the explosive growth of the Internet information. Making real time image steganalysis online to detect the malicious use of multimedia documents for covert communication has become more and more difficult. Traditional research on image steganalysis mainly focus on how to improve the accuracy of Steganalysis, few of them pay much attention on the efficiency of the Image Steganalysis technology and the architecture optimizing for Steganalysis service providing. Regarding this problem, this paper proposed a Image Steganalysis Technique Based on Cloud Computing and BP Neutral Network, the structure and workflow of the system is introduced in detail. The result of simulation experiment proves that the import of cloud computing architecture can apparently improve the efficiency of image Steganalysis in the environment of internet and the accuracy of Steganalysis algorithm is acceptable instead of being greatly affected by system load.

Secure Message Embedding in 3D Images

Abstract: Due to the evolution in digital communications, sending a sheltered message, where interferers are prying for any useful piece of information, has become a rigid mission. In this paper, a multiple layer message security scheme is proposed, utilizing 3D images. The proposed scheme is robust against any means of eavesdropping or intruding as it is comprised of four layers of security as follows: encryption using AES–128, encoding using a repetition code, least significant bit (LSB) steganography and jamming through the addition of noise. The proposed scheme is compared with its counterparts from the literature and is shown to exhibit excellent traits, in terms of being reversible and its ability to carry out blind extraction of the data as well as withstanding geometrical attacks. Furthermore, the proposed scheme exhibits very good performance in terms of the mean squared error (MSE) and the peak signal to noise ratios (PSNR).

A steganalysis method in the DCT domain

Abstract: In this paper, we propose a steganalysis method based on the principle of additive operator, which chooses non-zero AC coefficients as carriers, with secret information independent of the carrier information flow. In the proposed method, AC coefficient statistical and energy features are initially extracted and used to construct a 3D feature vector. By employing the principle of Fisher linear discriminate analysis, a flexible discriminate classifier suitable for the extracted features is designed to improve detection performance. We infer and confirm theory of change in the statistical and energetic characteristics of the AC coefficient before and after additive steganography. The effectiveness of the proposed method is proven by the experiments. Moreover, the proposed method consistently outperforms related methods.

Large Scale Image Steganalysis Based on MapReduce

Abstract: Steganalysis is the opposite art to steganography, whose goal is to detect whether or not the seemly innocent objects like image hiding message. Image steganalysis is important research issue of information security field. With the development of steganography technology, steganalysis becomes more and more difficult. Regarding the problem of improving the performance of image steganalysis, many research work have been done. Based on current research, large scale training set will be the feasible means to improve the steganalysis performance. Classic classifier is out of work to deal with large scale images steganalysis. In this paper, a parallel Support Vector Machines based on MapReduce is used to build the steganalysis classifier according to large scale training samples. The efficiency of the proposed method is illustrated with an experiment analysis.

Detection of Steganographic Intrusion in Cloud

Abstract: The customer’s try to migrate the data from desktop to cloud.. The data stored in cloud is targeted by potential threat. A potential threat is caused due to malicious hacker attack or data leakage in cloud. These threats in cloud environment arises the need for providing secure and safe information security system that can protect the data that is outsourced. In this paper we propose a framework for Intrusion Detection (ID) which identifies steganographic intrusion in cloud. General Terms Security, Intrusion detection system

An Evaluation of the Robustness Chi-Square Steganalysis Method on Wav Audio Files with Various Content

Abstract: This paper presents the performance of the chi-square method for steganalysis purposes on audio files through various content. Predetermined rates of stego data have been embedded into 4500 distinct cover wav files via LSB (Least Significant Bit) method using three types of audio input files: music, human voice, animal sounds. Results of the experiment have revealed that hidden data in wav files containing dissimilar audio samples cannot be easily detected by chi-square steganalysis method. However, wav files containing similar audio samples demonstrate better performance compared to dissimilar samples.