کاهش ابعاد روش پنهان‌شکنی CDF با استفاده از یک روش انتخاب ویژگی مبتنی بر تئوری گراف
الموضوعات :
سعید آزادی‌فر
1
(دانشگاه صنعتی خواجه نصیرالدین طوسی)
سیدحسین خواسته
2
(دانشگاه صنعتی خواجه نصیرالدین طوسی)
محمدهادی ادریسی
3
(دانشگاه اصفهان)
الکلمات المفتاحية: پنهان‌شکنیپنهان‌نگاریانتخاب ویژگیکاهش بعد,
ملخص المقالة :
پنهان‌شکنی دانش کشف حضور داده پنهان در یک رسانه پوششی است. هدف پنهان‌شکنی جلوگیری از رسیدن روش‌های پنهان‌نگاری به اهداف خود می‌باشد. یکی از معروف‌ترین روش‌های پنهان‌شکنی روش CDF است که در این پژوهش استفاده شده است. یکی از چالش‌های عمده در مسئله پنهان‌شکنی تصاویر تعداد زیاد ویژگی‌های استخراج‌شده برای این کار است. مجموعه‌های داده‌ای با ابعاد بالا از دو جهت باعث کاهش عملکرد پنهان‌شکنی می‌شود. از یک طرف با افزایش ابعاد داده‌‌ها، حجم محاسبات افزایش پیدا می‌کند و از طرف دیگر مدلی که بر اساس داده‌های با ابعاد بالا ساخته می‌شود دارای قابلیت تعمیم پایینی است و احتمال بیش‌برازش افزایش می‌یابد. در نتیجه، کاهش ابعاد مسئله می‌تواند هم پیچیدگی محاسباتی را کاهش داده و هم باعث بهبود عملکرد پنهان‌شکنی شود. در این مقاله تلاش شده با ترکیب مفهوم زیرگراف کامل بیشینه وزن‌دار و معیار مرکزیت یال و در نظر گرفتن مناسب‌بودن هر ویژگی، ویژگی‌های تأثیرگذار و دارای حداقل افزونگی به‌عنوان ویژگی‌های نهایی انتخاب شوند. نتایج شبیه‌سازی بر روی مجموعه داده‌های SPAM و CC-PEV نشان داد روش پیشنهادی دارای عملکرد مناسبی است و به دقت حدود 96% در تشخیص جاسازی داده در تصاویر دست پیدا کرده و همچنین این روش در مقایسه با روش‌های شناخته‌شده قبلی دارای دقت بالاتری است.
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