به سمت نهان کاوی بهبود یافته
ترجمه نشده

به سمت نهان کاوی بهبود یافته

عنوان فارسی 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
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات، امنیت اطلاعات
کلمات کلیدی فارسی: انتخاب پوششی، پنهان نگاری، نهان کاوی، خوشه بندی
کلمات کلیدی انگلیسی: Cover selection, steganography, steganalysis, clustering
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2955113
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
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 8
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14049
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Related Work


III. Proposed Method


IV. Experimental Results


V. Conclusion


Authors


Figures


References

نمونه متن انگلیسی 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]–[10].

  • اشتراک گذاری در

دیدگاه خود را بنویسید:

تاکنون دیدگاهی برای این نوشته ارسال نشده است

به سمت نهان کاوی بهبود یافته
نوشته های مرتبط
مقالات جدید
نماد اعتماد الکترونیکی
پیوندها