مقاله انگلیسی رایگان در مورد به سمت نهان کاوی بهبود یافته – IEEE 2019

IEEE

 

مشخصات مقاله
ترجمه عنوان مقاله به سمت نهان کاوی بهبود یافته: هنگامی که از انتخاب پوششی در پنهان نگاری استفاده می شود
عنوان انگلیسی مقاله Towards Improved Steganalysis: When Cover Selection is Used in Steganography
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۸ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه IEEE
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۶۴۱ در سال ۲۰۱۸
شاخص H_index ۵۶ در سال ۲۰۱۹
شاخص SJR ۰٫۶۰۹ در سال ۲۰۱۸
شناسه ISSN ۲۱۶۹-۳۵۳۶
شاخص Quartile (چارک) Q2 در سال ۲۰۱۸
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط مهندسی کامپیوتر
گرایش های مرتبط مهندسی الگوریتم و محاسبات، امنیت اطلاعات
نوع ارائه مقاله
ژورنال
مجله / کنفرانس دسترسی – IEEE Access
دانشگاه  Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
کلمات کلیدی انتخاب پوششی، پنهان نگاری، نهان کاوی، خوشه بندی
کلمات کلیدی انگلیسی  Cover selection, steganography, steganalysis, clustering
شناسه دیجیتال – doi
https://doi.org/10.1109/ACCESS.2019.2955113
کد محصول  E14049
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
I. Introduction
II. Related Work
III. Proposed Method
IV. Experimental Results
V. Conclusion
Authors
Figures
References

 

بخشی از متن مقاله:
Abstract

This paper proposes an improved steganalytic method when cover selection is used in steganography. We observed that the covers selected by existing cover selection methods normally have different characteristics from normal ones, and propose a steganalytic method to capture such differences. As a result, the detection accuracy of steganalysis is increased. In our method, we consider a number of images collected from one or more target (suspected but not known) users, and use an unsupervised learning algorithm such as k-means to adapt the performance of a pre-trained classifier towards the cover selection operation of the target user(s). The adaptation is done via pseudo-labels from the suspected images themselves, thus allowing the re-trained classifier more aligned with the cover selection operation of the target user(s). We give experimental results to show that our method can indeed help increase the detection accuracy, especially when the percentage of stego images is between 0.3 and 0.7.

Introduction

Steganography is the art of covert communication, aiming to transmit data secretly through public channels without drawing suspicion, and steganalysis aims to disclose the secret transmission by analyzing suspected media [1]. Modern steganalytic methods use supervised machine learning to investigate the models of the covers and the stegos. Features are extracted from a set of images to train a common steganalytic classifier, which is then used to distinguish real stego images from normal (cover) images without any hidden information [2], [3]. The ensemble classifier [4] is widely used to enhance the performance by using multiple classifiers. The feature extraction and machine learning based steganalysis has been proved to be efficient. The most popular feature set is SRM (Spatial Rich Model) [5], which are the fourth order co-occurrence matrices for describing the dependencies among different pixels. After SRM, some improved feature extraction methods are proposed [6], [7]. In PSRM (Projections of Spatial Rich Model) [6], neighboring residual samples are projected onto a set of random vectors and the histograms of the projections are taken as the feature. The feature set maxSRMd2 [7] is a variant of SRM that makes use of the modification probabilities of cover elements during data embedding, which is called probabilistic selection channel. Recently, deep learning based steganalysis has also achieved good performances with enough training data [8]–[۱۰].

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