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طلعتی, سعید, اصفهانی, رضا. (1402). ارائه مدل جدید نهان کاوی هوشمند تصویر مبتنی بر شبکه عصبی MLP. پدافند الکترونیکی و سایبری, 14(4), 21-32.
سعید طلعتی; رضا اصفهانی. "ارائه مدل جدید نهان کاوی هوشمند تصویر مبتنی بر شبکه عصبی MLP". پدافند الکترونیکی و سایبری, 14, 4, 1402, 21-32.
طلعتی, سعید, اصفهانی, رضا. (1402). 'ارائه مدل جدید نهان کاوی هوشمند تصویر مبتنی بر شبکه عصبی MLP', پدافند الکترونیکی و سایبری, 14(4), pp. 21-32.
طلعتی, سعید, اصفهانی, رضا. ارائه مدل جدید نهان کاوی هوشمند تصویر مبتنی بر شبکه عصبی MLP. پدافند الکترونیکی و سایبری, 1402; 14(4): 21-32.

ارائه مدل جدید نهان کاوی هوشمند تصویر مبتنی بر شبکه عصبی MLP

پدافند غیرعامل
دوره 14، شماره 4 - شماره پیاپی 56، دی 1402، صفحه 21-32    اصل مقاله (1.38 M)
نوع مقاله: مقاله پژوهشی
نویسندگان
سعید طلعتی1؛ رضا اصفهانی* 2
1دانشجوی دکتری دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران
2استادیار گروه مخابرات دانشگاه جامع امام حسین(ع)، تهران، ایران
تاریخ دریافت: 11 فروردین 1402،  تاریخ بازنگری: 27 خرداد 1402،  تاریخ پذیرش: 06 تیر 1402 
چکیده
پیشرفت روزافزون مخابرات، انتقال امن را به یکی از مهم‌ترین مسائل امروزه تبدیل کرده است. از آنجا که در تصویر ظرفیت پنهان شدن بالایی وجود دارد استفاده از پنهان­نگاری تصویر نسبت به سایر روش­های پنهان­نگاری بسیار مرسوم­تر است. در این مقاله از روش پنهان­نگاری به روش تبدیل موجک استفاده‌شده که نتایج نشان می­دهد این روش از مقاومت بالایی بهره می­برد. و برای تحلیل تصاویر پنهان‌شده به روش تبدیل موجک الگوریتمی با استفاده از ویژگی‌های ماتریس  (GLCM)و بردارهای هم‌رخدادی (DCL) ارائه‌شده است. پس از بررسی این  مقادیر در تصاویر اصلی و کاور، ویژگی‌های متفاوت بین این تصاویر استخراج و برای آموزش شبکه عصبی چندلایه  (MLP) استفاده می‌شوند. مرحله طبقه‌بندی با استفاده از لایه‌های این شبکه عصبی انجام‌شده و الگوریتم پیشنهادی برای پایگاه داده 200 تصویر استاندارد (Casia-Iris)  تست‌شده است. دقت آشکارسازی %90 تصاویر پنهان‌شده در روش پیشنهادی برتری این روش نهان­کاوی در برابر سایر روش­ها را نشان می­دهد.
کلیدواژه‌ها
پنهان‌نگاری؛ نهان‌کاوی؛ تبدیل موجک؛ ماتریس هم‌رخدادی؛ شبکه عصبیMLP
عنوان مقاله [English]
Presenting a New Method of Image Steganalysis Based on MLP Neural Network
نویسندگان [English]
Saeed Talati1؛ ٍEsfahani Reza2
1Phd
2Scientific Department of Communication
چکیده [English]
The ever-increasing development of telecommunications has made secure transmission one of the most important issues today. Since there is a high hiding capacity in the image, the use of image encryption is much more common than other methods of encryption. This article uses the covert imaging technique with the wavelet transform method, and the results show that this method has high resistance. For the analysis of hidden images, an algorithmic wavelet transform method using matrix features (GLCM) and co-occurrence vectors (DCL) is presented. After checking these values in the original and cover images, the different features between these images are extracted and used to train the multilayer neural network (MLP). The classification stage has been performed using the layers of this neural network and the proposed algorithm has been tested for a database of 200 standard images (Casia-Iris). The detection accuracy of 90% of the hidden images in the proposed method shows the superiority of this hidden mining method over other methods.
کلیدواژه‌ها [English]
Steganography, Steganalysis, Wavelet Transform, Co-occurrence Matrix, MLP Neural Network
مراجع

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