To recognize F5-like (such as F5 and nsF5) steganographic algorithm from multi-class stego images, a recognition algorithm based on the identifiable statistical feature (IDSF) of F5-like steganography is proposed in this paper. First, this paper analyzes the special modification ways of F5-like steganography to image data, as well as the special changes of statistical properties of image data caused by the modifications. And then, by constructing appropriate feature extraction sources, the IDSF of F5-like steganography distinguished from others is extracted. Lastly, based on the extracted IDSFs and combined with the training of SVM (Support Vector Machine) classifier, a recognition algorithm is presented to recognize F5-like stego images from images set consisting of a large number of multi-class stego images. A series of experimental results based on the detection of five types of typical JPEG steganography (namely F5, nsF5, JSteg, Steghide and Outguess) indicate that, the proposed algorithm can distinguish F5-like stego images reliably from multi-class stego images generated by the steganography mentioned above. Furthermore, even if the types of some detected stego images are unknown, the proposed algorithm can still recognize F5-like stego images correctly with high accuracy.
The 1R(Infra-Red) spectrum and PL(Photoluminescence) of the antiferromagnetic pure
The purpose of this study is to develop a patient care standard which is the basis of unit based quality assurance. The subjects were 570 nurses of 6 hospitals is Seoul. Patient Care Standards were developed from 3 times of clinical Nurses Association's workshop & the joint meeting of Clinical Nurses Association & the Korean Nurses Academic Socity of Nursing Administration. Respondents were instructed to rate of the 2 types of 5 - point Likert type questionnaire(one is the level of perceived importance, the other is the level of actual performance) Findings of this study were as follows 1. As a results of reliability analysis, each questionnaire ranged from