نهان کاوی گفتار در بیت های کم ارزش بر مبنای درصد نمونه های مجاور یکسان

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه مهندسی کامپیوتر، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران

2 استادیار گروه مهندسی کامپیوتر، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران

3 دانشیار گروه مهندسی کامپیوتر، واحد دزفول، دانشگاه آزاد اسلامی، دزفول، ایران

چکیده

عمومیت فایل­های صوتی، اغلب توجه مهاجمین و عناصر مخرب را برای استفاده از این حامل، جهت پوشش­دهی ارتباطات محرمانه خود جلب می‌نماید. گستردگی استفاده از این قالب­ها، به‌همراه رویکردهای متعدد و مدرنی که برای نهان­نگاری در فایل­های صوتی طراحی شده­اند،   می­توانند فضای سایبری را به محیطی نا امن بدل نمایند. در راستای مقابله با این تهدیدات، امروزه روش­های متعدد نهان­کاوی ابداع شده‌اند که با دقت بالایی قادر به تحلیل آماری قالب­های مختلف صوتی، مانند MP3 و VoIp هستند. در میان راه­حل­های ارائه‌شده، ترکیب روش­های پردازش سیگنال و یادگیری ماشین، امکان ایجاد نهان­کاوهایی با دقت بسیار بالا را فراهم نموده است. با این وجود، از آنجا که ویژگی­های آماری فایل­های صوتی گفتاری متفاوت از نمونه­های دیگر صوتی است، روش­های جاری نهان­کاوی قادر نیستند به شکل مؤثری فایل­های حامل گفتاری را تشخیص دهند. مشکل دیگر، ابعاد بالای تحلیلی است که به شکل چشمگیری هزینه پیاده­سازی را افزایش می­دهد. در پاسخ به مشکلات ذکرشده، این مقاله ویژگی یک­بعدی "درصد نمونه­های مجاور یکسان" را به‌عنوان فاکتور جداسازی نمونه­های نهان­نگاری شده از پاک مطرح می­کند. نتایج نشانگر حساسیت 82/99% نهان­کاو طراحی‌شده با استفاده از دسته­بند تابع عضویت گاوسی، در نرخ نهان­نگاری 50% است. علاوه بر این، این نهان­کاو قادر است با دقت مطلوبی حجم پیام مخفی‌شده را تخمین بزند. عملکرد الگوریتم طراحی‌شده بر روی یک پایگاه داده متشکل از نمونه­های موسیقی کلاسیک نیز ارزیابی شده و نتایج حاکی از کارایی 2/81% آن هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Speech Steganalysis of Least Significant Bits Based on the Percentage of Equal Adjacent Samples

نویسندگان [English]

  • S. Yazdanpanah 1
  • M. Kheyrandish 2
  • M. Mosleh 3
1 Faculty of computer science, Islamic Azad University, Khorramabad Branch, Khorramabad, Iran
2 Department of Computer engineering . Dezful Branch. Islamic Azad University. Dezful. Iran
3 Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
چکیده [English]

The popularity of audio formats usually attracts the attention of intruders and criminals to use this medium as a cover for establishing their secret communications. The extensive use of this formats, along with       various modern techniques, designed for audio steganography, can cause the cyber spaces to be insecure environments. In order to deal with threats, some audio steganalysis techniques have been presented that statistically analyze various audio formats, such as music, MP3, and VoIP, efficiently. Among the presented approaches, combining the techniques of signal processing and machine learning has made possible the creation of steganalyzers that are highly accurate. However, since the statistical properties of audio files differ from purely speech ones, the current steganalysis methods cannot detect speech stego files,            accurately. Another issue is the large number of analysis dimensions which increase the implementation cost, significantly. As response to these issues, this paper proposes the percentage of equal adjacent      samples (PEAS) feature, as a one-dimensional feature for speech steganalysis. Using a classifier, based on the Gaussian membership function, on stego instances with 50% embedding ratio, the evaluation results for the designed steganalyzer, show a sensitivity of 99.82%. Additionally, it can efficiently estimate the length of a hidden message with the desirable accuracy. Also, the PEAS steganalysis was evaluated on a database, containing classic music instances, and the results show an 81.2% efficient performance.
 

کلیدواژه‌ها [English]

  • Speech steganalysis
  • audio steganalysis
  • digital signal processing
  • LSB
  • Steganography
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دوره 9، شماره 1 - شماره پیاپی 33
شماره پیاپی 33، فصلنامه بهار
اردیبهشت 1400
صفحه 75-90
  • تاریخ دریافت: 18 فروردین 1399
  • تاریخ بازنگری: 21 خرداد 1399
  • تاریخ پذیرش: 05 آبان 1399
  • تاریخ انتشار: 01 اردیبهشت 1400