Yapay Zeka Güvenliği : Machine Learning & Deep Learning & Computer Vision Security
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Yapay Zeka Güvenliği : Machine Learning & Deep Learning & Computer Vision Security http://deeplab.co cihan@deeplab.co
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Yapay Zeka Güvenliği : Machine Learning & Deep Learning & Computer Vision Security
- 2. AI Security Machine Learning, Deep Learning and Computer Vision Security Cihan Özhan | DeepLab.co | Founder & Autonomous System Developer
- 8. DeepLab : Teknolojiler ML/DL/CV Odaklı • Teknolojiler – Go, Python, C/C++, Rust, C# – PyTorch, TensorFlow, Keras, scikit-learn • Web, Mobile, IoT/Edge ve Back-End olarak… – OpenCV – … ve farklı onlarca araç-gereç… • Cloud Computing – AWS Machine Learning – Google Cloud Machine Learning – IBM Watson Machine Learning – Microsoft Azure Machine Learning – … ve farklı birçok Cloud çözüm… • Distributed Systems – Distributed Databases – Distributed Deep Learning
- 9. Klişe No : 85496477
- 10. AI Objects • Image • Text • File • Voice • Video • Data • 3D Object
- 11. ML/DL Applications • Image Classification • Pose Estimation • Face Recognition • Face Detection • Object Detection • Question Answering System • Semantic Segmentation • Text Classification • Text Recognition • Sentiment Analysis
- 12. ML/DL Algorithms • Classification (Supervised) • Clustering (Unsupervised) • Regression (Supervised) • Generative Models (Semi-Supervised) • Dimensionality Reduction (Unsupervised) • Reinforcement Learning (Reinforcement)
- 13. MLaaS? Machine Learning as a Service ML/DL algoritma ve yazılımlarının, bulut bilişim hizmetlerinin bir bileşeni olarak sunulması modeline denir. MLaaS = (SaaS + [ML/DL/CV])
- 14. Hidden Technical Debt in Machine Learning Systems https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Genellikle tüm ekip, odak ve kaynakların yönlendirildiği alan!
- 15. Model Lifecycle Machine Learning Model Development Lifecycle
- 16. Model Lifecycle Machine Learning Model Development Lifecycle Biz buradan başlıyoruz! ML model hazırlık süreci Angarya ama mecburi görev: Veriyi hazırlamak! Modeli hazırlamışız! Modeli veri ile eğitiyoruz. Cloud ya da On-Premise Eğitilmiş modeli test verisi ile test ettik! Eğitilen model programsal ortam için paketlenir. Yayın sonrası: Model sürekli izlenir.
- 17. Machine Learning projesi Nasıl Yayınlanır?
- 18. MartinFowler.com
- 19. MartinFowler.com
- 21. Temel Güvenlik Sorunları Kasıtlı Hatalar Kasıtsız Hatalar Perturbation Attack Reward Hacking Poisoning Attack Side Effects Model Inversion Distributional Shifts Membership Inference Natural Adversarial Examples Model Stealing Common Corruption Reprogramming ML system Incomplete Testing Adversarial Example in Pyhsical Domain Malicious ML provider recovering training data Attacking the ML supply chain Backdoor ML Exploit Software Dependencies
- 23. Adversarial Attack : Image (https://adversarial.io/)
- 27. Adversarial Attack : Speech-to-Text (https://people.eecs.berkeley.edu/~daw/papers/audio-dls18.pdf)
- 29. Adversarial Attack : NLP https://arxiv.org/pdf/2005.05909.pdf https://github.com/QData/TextAttack
- 31. Adversarial Attack : Remote Sensing (https://arxiv.org/pdf/1805.10997.pdf)
- 32. Adversarial Attack : Satellite (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8823003)
- 33. Adversarial Attack : Military https://spectrum.ieee.org/automaton/artificial-intelligence/embedded-ai/adversarial-attacks-and-ai-systems
- 34. Adversarial Attack : Military https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-IST-160/MP-IST-160-S1-5.pdf
- 35. Adversarial Attack : Autonomous Driving https://web.cs.ucla.edu/~miryung/Publications/percom2020-autonomousdriving.pdf https://github.com/ITSEG-MQ/Adv-attack-and-defense-on-driving-model
- 36. Security Research of Tesla Autopilot (40 Pages) https://keenlab.tencent.com/en/whitepapers/Experimental_Security_Research_of_Tesla_Autopilot.pdf
- 37. Endüstriyel Yapay Zeka ve Otonom Araçlar - Cihan Özhan https://www.youtube.com/watch?v=7RS4tLMfoo0
- 39. Exploit Software Dependencies • Algoritmaları değil, sistem bağımlı olduğu yazılımların güvenlik açıklarından faydalanır. • Önlem: – Security Scan – Security Reports – Dikkat Et : Wrappers ve Pre-Build Environment – Az Dependency Kullan – Dependency Management Tools • Synk : Synk.io • Python Poetry : python-poetry.org • Bandit : – Bandit is a tool designed to find common security issues in Python code. – https://github.com/PyCQA/bandit • pyup.io/safety • requires.io – vb…
- 40. Tool/Library Security (TensorFlow) • TensorFlow(gibi araçlar) internal iletişim için tasarlanmıştır, güvensiz(untrusted) ağlarda çalışmak için değil. • Bu araçlar(ModelServer vb.) built-in yetkilendirmeye sahip değildir. • Dosyaları okuyup yazabilir, network üzerinden veri alıp gönderebilir… • (!) TensorFlow Models as Programs • (!) Running Untrusted Models • (!) Accepting Untrusted Inputs https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md
- 42. Cihan Özhan Bağlantılar • cihanozhan.com • linkedin.com/in/cihanozhan • medium.com/@cihanozhan • youtube.com/user/OracleAdam • twitter.com/UnmannedCode • github.com/cihanozhan E-Mail • cihan@deeplab.co
Editor's Notes
- - https://christophergs.com/machine%20learning/2019/03/17/how-to-deploy-machine-learning-models/
- https://medium.com/analytics-vidhya/machine-learning-models-to-production-72280c3cb479 Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform (https://arxiv.org/ftp/arxiv/papers/1810/1810.07159.pdf)
- https://data-flair.training/blogs/tensorflow-security/ https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md