ارائه روشی مقاوم در برابر حملات تخاصمی با استفاده از فرایندهای گوسی مقیاس‌پذیر و رأی‌گیری
محورهای موضوعی : مهندسی برق و کامپیوترمهران صفایانی 1 , پویان َشالبافان 2 , سید هاشم احمدی 3 , مهدیه فلاح علی آبادی 4 , عبدالرضا میرزائی 5
1 - دانشگاه صنعتی اصفهان،دانشکده مهندسی برق و کامپیوتر
2 - دانشگاه صنعتی اصفهان،دانشکده مهندسی برق و کامپیوتر
3 - دانشگاه صنعتی اصفهان،دانشکده مهندسی برق و کامپیوتر
4 - دانشگاه صنعتی اصفهان،دانشکده مهندسی برق و کامپیوتر
5 - دانشگاه صنعتی اصفهان،دانشکده مهندسی برق و کامپیوتر
کلید واژه: شبکه‌های عصبی, فرایندهای گوسی, فرایندهای گوسی مقیاس‌پذیر, مثال‌های تخاصمی,
چکیده مقاله :
در سال‌های اخیر، مسئله‌ای تحت عنوان آسیب‌پذیری مدل‌های مبتنی بر یادگیری ماشین مطرح گردیده است که نشان می‌دهد مدل‌های یادگیری در مواجهه با آسیب‌پذیری‌ها از مقاومت بالایی برخوردار نیستند. یکی از معروف‌ترین آسیب‌ها یا به بیان دیگر حملات، تزریق مثال‌های تخاصمی به مدل می‌باشد که در این مورد، شبکه‌های عصبی و به ویژه شبکه‌های عصبی عمیق بیشترین میزان آسیب‌پذیری را دارند. مثال‌های تخاصمی، از طریق افزوده‌شدن اندکی نویز هدفمند به مثال‌های اصلی تولید می‌شوند، به طوری که از منظر کاربر انسانی تغییر محسوسی در داده‌ها مشاهده نمی‌شود اما مدل‌های یادگیری ماشینی در دسته‌بندی داده‌ها به اشتباه می‌افتند. یکی از روش‌های موفق جهت مدل‌کردن عدم قطعیت در داده‌ها، فرایندهای گوسی هستند که چندان در زمینه مثال‌های تخاصمی مورد توجه قرار نگرفته‌اند. یک دلیل این امر می‌تواند حجم محاسباتی بالای این روش‌ها باشد که کاربردشان در مسایل واقعی را محدود می‌کند. در این مقاله از یک مدل فرایند گوسی مقیاس‌پذیر مبتنی بر ویژگی‌های تصادفی بهره گرفته شده است. این مدل علاوه بر داشتن قابلیت‌های فرایندهای گوسی از جهت مدل‌کردن مناسب عدم قطعیت در داده‌ها، از نظر حجم محاسبات هم مدل مطلوبی است. سپس یک فرایند مبتنی بر رأی‌گیری جهت مقابله با مثال‌های تخاصمی ارائه می‌گردد. همچنین روشی به نام تعیین ارتباط خودکار به منظور اعمال وزن بیشتر به نقاط دارای اهمیت تصاویر و اعمال آن در تابع هسته فرایند گوسی پیشنهاد می‌گردد. در بخش نتایج نشان داده شده که مدل پیشنهادشده عملکرد بسیار مطلوبی در مقابله با حمله علامت گرادیان سریع نسبت به روش‌های رقیب دارد.
In recent years, the issue of vulnerability of machine learning-based models has been raised, which shows that learning models do not have high robustness in the face of vulnerabilities. One of the most well-known defects, or in other words attacks, is the injection of adversarial examples into the model, in which case, neural networks, especially deep neural networks, are the most vulnerable. Adversarial examples are generated by adding a little purposeful noise to the original examples so that from the human user's point of view there is no noticeable change in the data, but machine learning models make mistakes in categorizing the data. One of the most successful methods for modeling data uncertainty is Gaussian processes, which have not received much attention in the field of adversarial examples. One reason for this could be the high computational volume of these methods, which limits their used in the real issues. In this paper, a scalable Gaussian process model based on random features has been used. This model, in addition to having the capabilities of Gaussian processes for proper modeling of data uncertainty, is also a desirable model in terms of computational cost. A voting-based process is then presented to deal with adversarial examples. Also, a method called automatic relevant determination is proposed to weight the important points of the images and apply them to the kernel function of the Gaussian process. In the results section, it is shown that the proposed model has a very good performance against fast gradient sign attack compared to competing methods.
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