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Adversarial Attacks on Deep Learning Systems

الهجمات الخادعة ضد شبكات التعلم العميق

1975   0   140   0 ( 0 )
 Publication date 2018
and research's language is العربية
 Created by محمد زاهر عيروط




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Deep learning is at the heart of the current rise of artificial intelligence. In the field of Computer Vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently lead to a large influx of contributions in this direction. This article presents a survey on adversarial attacks on deep learning in Computer Vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them


Artificial intelligence review:
Research summary
يعتبر التعلم العميق القلب النابض للذكاء الصنعي في السنوات الأخيرة، حيث يستخدم في تطبيقات متنوعة مثل السيارات ذاتية القيادة والتحليلات الطبية. ومع ذلك، فإن الهجمات الخادعة أصبحت عائقًا كبيرًا أمام توظيف هذه التقنيات بشكل آمن. تتناول هذه الورقة البحثية تعريف الهجمات الخادعة، طرق تنفيذها، وتأثيرها على الأنظمة المختلفة، مع التركيز على الأنظمة الطبية. كما تستعرض الورقة استراتيجيات الدفاع المختلفة ضد هذه الهجمات، مثل الدفاع التفاعلي والدفاع الاستباقي، وتقدم أمثلة على الهجمات الخادعة المادية وكيفية التصدي لها. الهدف من البحث هو توضيح خطورة الهجمات الخادعة وضرورة تطوير استراتيجيات دفاعية فعالة لحماية الأنظمة التي تعتمد على التعلم العميق.
Critical review
دراسة نقدية: تقدم هذه الورقة البحثية نظرة شاملة ومفصلة حول الهجمات الخادعة ضد شبكات التعلم العميق، وتستعرض العديد من الأمثلة والتطبيقات العملية. ومع ذلك، يمكن القول أن الورقة تفتقر إلى بعض التفاصيل العملية حول كيفية تنفيذ الدفاعات المقترحة بشكل فعلي في الأنظمة الحقيقية. كما أن التركيز الكبير على الجانب النظري قد يجعل من الصعب على القراء غير المتخصصين فهم بعض النقاط المعقدة. بالإضافة إلى ذلك، يمكن تحسين الورقة بإضافة دراسات حالة عملية توضح فعالية الدفاعات المقترحة في مواجهة الهجمات الخادعة في بيئات حقيقية.
Questions related to the research
  1. ما هي الهجمات الخادعة ولماذا تعتبر مشكلة في أنظمة التعلم العميق؟

    الهجمات الخادعة هي تعديلات طفيفة على مدخلات النموذج تؤدي إلى مخرجات خاطئة. تعتبر مشكلة لأنها تهدد دقة وموثوقية الأنظمة التي تعتمد على التعلم العميق في تطبيقات حساسة مثل السيارات ذاتية القيادة والتحليلات الطبية.

  2. ما هي استراتيجيات الدفاع الرئيسية ضد الهجمات الخادعة التي تناولتها الورقة؟

    تناولت الورقة استراتيجيتين رئيسيتين للدفاع ضد الهجمات الخادعة: الدفاع التفاعلي، الذي يعتمد على اكتشاف الهجمات ورفضها، والدفاع الاستباقي، الذي يعتمد على تدريب النموذج باستخدام أمثلة عدائية لتحسين متانته.

  3. كيف يمكن للهجمات الخادعة التأثير على الأنظمة الطبية المعتمدة على التعلم العميق؟

    يمكن للهجمات الخادعة التأثير بشكل كبير على الأنظمة الطبية من خلال تقديم تشخيصات خاطئة، مما قد يؤدي إلى قرارات علاجية غير صحيحة. هذا يبرز الحاجة إلى تطوير استراتيجيات دفاعية فعالة لحماية هذه الأنظمة.

  4. ما هي التحديات الرئيسية في تطبيق الدفاعات ضد الهجمات الخادعة في البيئات الحقيقية؟

    تشمل التحديات الرئيسية في تطبيق الدفاعات ضد الهجمات الخادعة في البيئات الحقيقية صعوبة تنفيذ الدفاعات بشكل فعال دون التأثير على أداء النظام، والحاجة إلى معالجة الظروف البيئية المختلفة، مثل التغيرات في الإضاءة وزوايا الرؤية.


References used
Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno: “Robust Physical-World Attacks on Deep Learning Models”, 2017; arXiv:1707.08945.
Wieland Brendel, Jonas Rauber: “Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models”, 2017; arXiv:1712.04248.
Samuel G. Finlayson, Isaac S. Kohane: “Adversarial Attacks Against Medical Deep Learning Systems”, 2018; arXiv:1804.05296.
Naveed Akhtar: “Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey”, 2018; arXiv:1801.00553.
Ian J. Goodfellow, Jonathon Shlens: “Explaining and Harnessing Adversarial Examples”, 2014; arXiv:1412.6572.
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville: “Generative Adversarial Networks”, 2014; arXiv:1406.2661.
Pouya Samangouei, Maya Kabkab: “Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models”, 2018; arXiv:1805.06605.
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