آشکارسازی فشرده‌سازی JPEG مضاعف با استفاده از شبکه‌های عصبی عمیق در حوزه مکان

محورهای موضوعی : انرژی های تجدیدپذیر

محمد رحمتی ¹ (دانشکده مهندسی برق و کامپیوتر- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران)
فربد رزازی ² (دانشکده مهندسی برق و کامپیوتر- واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران)
علیرضا بهراد ³ (دانشکده فنی و مهندسی - دانشگاه شاهد، تهران، ایران)

تاریخ دریافت : 1400/02/20 تاریخ پذیرش : 1400/05/22 تاریخ انتشار : 1400/06/01

کلید واژه: فیلتر تطبیقی, صحت آشکارسازی, شبکه عصبی پیچشی, خودرمزگذار پیچشی, مکان‌یابی محل دست‌کاری,

چکیده مقاله :

: با افزایش علاقه‌مندی به فشرده نمودن تصاویر با فرمت فرمت گروه مشترک متخصصان عکاسی (JPEG)، یکی از مهم‌ترین مباحث در دست‌کاری تصاویر دیجیتال، یافتن روشی مناسب جهت آشکارسازی فشرده‌سازی JPEG مضاعف است. در این مقاله با معرفی یک فیلتر تطبیقی آموزش‌دیده بر پایه خودرمزگذار پیچشی (CAE) و در حوزه مکان، به این موضوع پرداخته می‌شود تا با حذف اطلاعات تداخلی ناشی از محتوای تصویر، آشکارسازی دقیق‌تری داشته باشیم. از آنجایی که شبکه عصبی پیچشی (CNN) توانسته عملکرد موفقی در طبقه‌بندی تصاویر داشته باشد، از این شبکه‌ها در قسمت طبقه‌بندی استفاده می‌شود. مدل پیشنهادی بر اساس CAE متوالی شده با CNN است که توانسته دقت آشکارسازی و حساسیت به ضرایب کیفیت (QFs) قابل قبولی را در دو سناریوی هم‌تراز و ناهم‌تراز ارائه نماید. این مدل توانسته در برخی از حالت ها، حساسیت نسبت به ضرایب کیفیت را تا 86 در صد در مقدار کاهش خطای نسبی (RER) بهبود دهد. آزمایش‌های دیگری از جمله مکان‌یابی محل دست‌کاری بر روی مجموعه داده RAISE برای ارزیابی روش پیشنهادی انجام شده است. این نتایج نشان‌دهنده عملکرد بسیار خوب این روش نسبت به الگوریتم‌های مشابه در شرایطی است که ضریب کیفیت فشرده‌سازی دوم بزرگ‌تر از ضریب کیفیت فشرده‌سازی اول باشد.

چکیده انگلیسی:

With the increasing interest in Joint Photographic Experts Group (JPEG) image compression, one of the most important issues in digital image manipulation is finding a proper method to detect double JPEG compression. This paper introduces a trained adaptive filter based on spatial-domain convolutional autoencoder (CAE). This filter can remove interference information caused by image content to have a more accurate detection. The convolutional neural network (CNN) has been widely employed for accurate image classification; therefore, a CNN is used in the classification part of the proposed algorithm. The proposed model is based on consecutive CAE with CNN, which is able to provide acceptable detection accuracy and sensitivity to quality factors (QFs) in two scenarios, i.e. aligned and non-aligned forgeries. This model improves the sensitivity to quality factors by up to 86% in the relative error reduction (RER) rate in some cases. Other experiments such as manipulation localization on the RAISE dataset have been performed to evaluate the proposed method. These results show the superior performance of this method compared to similar algorithms in the situations that the quality factor of the second compression is greater the quality factor of the first compression. 

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