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khalooei M, Homayounpour M M, Amirmazlaghani M. A survey on vulnerability of deep neural networks to adversarial examples and defense approaches to deal with them. JSDP 2023; 20 (2) : 8
URL: http://jsdp.rcisp.ac.ir/article-1-1205-fa.html
URL: http://jsdp.rcisp.ac.ir/article-1-1205-fa.html
خالوئی محمد، همایون پور محمد مهدی، امیرمزلقانی مریم. مروری بر آسیب‌پذیری شبکه‌های عصبی عمیق نسبت به نمونه‌های خصمانه و رویکردهای مقابله با آن‌ها. پردازش علائم و داده‌ها. 1402; 20 (2) :113-144
دانشگاه صنعتی امیرکبیردانشگاه صنعتی امیرکبیر
چکیده: (569 مشاهده)
امروزه شبکه‌های عصبی به‌عنوان بارزترین ابزار مطرح در هوش‌مصنوعی و یادگیری ماشین شناخته شده و در حوزه‌های مالی و بانکداری، کسب و کار، تجارت، سلامت، پزشکی، بیمه، رباتیک، هواپیمایی، خودرو، نظامی و سایر حوزه‌ها مورد استفاده قرار می‌گیرند. در سال‌های اخیر موارد متعددی از آسیب‌پذیری شبکه‌های عصبی عمیق نسبت به حملاتی مطرح شده که غالباً با افزودن اختلالات جمع‌شونده و غیر جمع‌شونده بر داده ورودی ایجاد می‌شوند. این اختلالات با وجود نامحسوس بودن در ورودی از دیدگاه عامل انسانی، خروجی شبکه آموزش دیده را تغییر می‌دهند. به اقداماتی که شبکه‌های عصبی عمیق را نسبت به حملات مقاوم می‌نمایند، دفاع اطلاق می‌شود. برخی از روش‌های حمله مبتنی بر ابزارهایی نظیر گرادیان شبکه نسبت به ورودی، به دنبال شناسایی اختلال می‌باشند و برخی دیگر به تخمین آن ابزارها می‌پردازند و در تلاش هستند تا حتی بدون داشتن اطلاعاتی از آن‌ها، به اطلاعات آن‌ها دست پیدا کنند. رویکردهای دفاع نیز برخی روی تعریف تابع هزینه بهینه و همچنین معماری شبکه مناسب و برخی دیگر بر جلوگیری و یا اصلاح داده قبل از ورود به شبکه متمرکز می‌شوند. همچنین برخی رویکردها به تحلیل میزان مقاوم‌بودن شبکه نسبت به این حملات و ارائه محدوده اطمینان متمرکز شده‌اند. در این مقاله سعی شده است تا جدیدترین پژوهش‌ها در زمینه آسیب‌پذیری شبکه‌های عصبی عمیق بررسی و مورد نقد قرار گیرند و کارایی آن‌ها با انجام آزمایش‌هایی مقایسه شود. در آزمایشات صورت گرفته در بین حملات محصورشده به 
و 
، روش AutoAttack کارایی بسیار بالایی دارد. البته باتوجه به برتری روش AutoAttack نسبت به روش‌هایی نظیر MIFGSM، PGD و DeepFool این روش برای اجرا، مدت زمان بیشتری به خاطر ترکیبی بودن ساختار درونی آن نسبت به سایر روش‌های همردیف خود نیاز دارد. همچنین به مقایسه برخی از رویکردهای پرکاربرد دفاع در مقابل نمونه‌های خصمانه نیز پرداخته شد و از بین روش‌های مبتنی بر نواحی محصورشده به 
حول داده، روش آموزش خصمانه مبتنی بر مشتقات PGD با پارامترهای مشخص، از سایر روش‌ها بهتر در مقابل اغلب روش‌های حمله مقاوم بوده است. لازم به ذکر است که روش‌های مختلف حمله خصمانه و دفاع نسبت به آن حملات که در این مقاله مورد بررسی قرار گرفت است در یک قالب مناسب و منعطف کدنویسی شده است. این قالب کدنویسی به عنوان یک پشتوانه پایدار ویژه تحقیق و پژوهش در حوزه یادگیری ماشین استاندارد و یادگیری ماشین خصمانه ویژه پژوهشگران و علاقه‌مندان از طریق آدرسhttps://github.com/khalooei/Robustness-framework در دسترس قرار گرفته است.
و ، روش AutoAttack کارایی بسیار بالایی دارد. البته باتوجه به برتری روش AutoAttack نسبت به روش‌هایی نظیر MIFGSM، PGD و DeepFool این روش برای اجرا، مدت زمان بیشتری به خاطر ترکیبی بودن ساختار درونی آن نسبت به سایر روش‌های همردیف خود نیاز دارد. همچنین به مقایسه برخی از رویکردهای پرکاربرد دفاع در مقابل نمونه‌های خصمانه نیز پرداخته شد و از بین روش‌های مبتنی بر نواحی محصورشده به حول داده، روش آموزش خصمانه مبتنی بر مشتقات PGD با پارامترهای مشخص، از سایر روش‌ها بهتر در مقابل اغلب روش‌های حمله مقاوم بوده است. لازم به ذکر است که روش‌های مختلف حمله خصمانه و دفاع نسبت به آن حملات که در این مقاله مورد بررسی قرار گرفت است در یک قالب مناسب و منعطف کدنویسی شده است. این قالب کدنویسی به عنوان یک پشتوانه پایدار ویژه تحقیق و پژوهش در حوزه یادگیری ماشین استاندارد و یادگیری ماشین خصمانه ویژه پژوهشگران و علاقه‌مندان از طریق آدرسhttps://github.com/khalooei/Robustness-framework در دسترس قرار گرفته است.
شماره‌ی مقاله: 8
نوع مطالعه: كاربردي |
موضوع مقاله:
مقالات پردازش تصویر
دریافت: 1399/10/30 | پذیرش: 1402/4/14 | انتشار: 1402/7/30 | انتشار الکترونیک: 1402/7/30
دریافت: 1399/10/30 | پذیرش: 1402/4/14 | انتشار: 1402/7/30 | انتشار الکترونیک: 1402/7/30
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