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AI Security : Machine Learning, Deep Learning and Computer Vision Security

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October 05, 2021

AI Security : Machine Learning, Deep Learning and Computer Vision Security

http://deeplab.co
cihan [ at ] deeplab.co

A1c300131f2bb926474b5a5199e97ab4?s=128

CO

October 05, 2021
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  1. Cihan Özhan Founder of DeepLab, http://deeplab.co

  2. DeepLab : Technologies ML/DL/CV Based • Technologies – Go, Python,

    C/C++, Rust, C# – PyTorch, TensorFlow, Keras, scikit-learn • As Web, Mobile, IoT/Edge ve Back-End… – OpenCV – … and dozens of different tools and equipment… • Cloud Computing – AWS Machine Learning – Google Cloud Machine Learning – IBM Watson Machine Learning – Microsoft Azure Machine Learning – … and various cloud solutions… • Distributed Systems – Distributed Databases – Distributed Deep Learning
  3. AI Security Machine Learning, Deep Learning and Computer Vision Security

    Cihan Özhan | Founder of DeepLab | Developer, AI Engineer, AI Hacker, Data Master
  4. OWASP Top 10 List OWASP (Open Web Application Security Project)

  5. None
  6. AI Data Objects • Image • Text • File •

    Voice • Video • Data • 3D Object
  7. ML/DL Applications • Image Classification • Pose Estimation • Face

    Recognition • Face Detection • Object Detection • Question Answering System • Semantic Segmentation • Text Classification • Text Recognition • Sentiment Analysis • Industrial AI • Autonomous Systems • and more…
  8. ML/DL Algorithms • Classification (Supervised) • Clustering (Unsupervised) • Regression

    (Supervised) • Generative Models (Semi-Supervised) • Dimensionality Reduction (Unsupervised) • Reinforcement Learning (Reinforcement)
  9. MLaaS? Machine Learning as a Service MLaaS is the method

    in which ML/DL algorithms and software are offered as a component of cloud computing services. MLaaS = (SaaS + [ML/DL/CV])
  10. Hidden Technical Debt in Machine Learning Systems https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Usually the

    whole team, focus and area where resources are directed!
  11. Model Lifecycle Machine Learning Model Development Lifecycle

  12. Model Lifecycle Machine Learning Model Development Lifecycle We start here!

    ML model preparation process The chore but the imperative: Preparing the data! We prepared the model! We train the model with data. Cloud or On-Premise We tested the trained model with test data! The trained model is packaged for the programmatic environment. Post release: The model is constantly monitored.
  13. How to Publish a Machine Learning project?

  14. MartinFowler.com

  15. MartinFowler.com

  16. Machine Learning Security

  17. Basic Security Issues Intentional Issues Unintentional Issues 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
  18. None
  19. Adversarial Attack : Image (https://adversarial.io/)

  20. None
  21. https://openai.com/blog/adversarial-example-research/

  22. https://hackernoon.com/adversarial-attacks-how-to-trick-computer-vision-7484c4e85dc0

  23. Adversarial Attack : Speech-to-Text (https://people.eecs.berkeley.edu/~daw/papers/audio-dls18.pdf)

  24. https://arxiv.org/pdf/2006.03575.pdf

  25. Adversarial Attack : NLP https://arxiv.org/pdf/2005.05909.pdf https://github.com/QData/TextAttack

  26. None
  27. Adversarial Attack : Remote Sensing (https://arxiv.org/pdf/1805.10997.pdf)

  28. Adversarial Attack : Satellite (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8823003)

  29. Adversarial Attack : Military https://spectrum.ieee.org/automaton/artificial-intelligence/embedded-ai/adversarial-attacks-and-ai-systems

  30. Adversarial Attack : Military https://www.sto.nato.int/publications/STO%20Meeting%20Proceedings/STO-MP-IST-160/MP-IST-160-S1-5.pdf

  31. 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

  32. Security Research of Tesla Autopilot (40 Pages) https://keenlab.tencent.com/en/whitepapers/Experimental_Security_Research_of_Tesla_Autopilot.pdf

  33. Industrial AI and Autonomous Systems - Cihan Özhan (Turkish) https://www.youtube.com/watch?v=ID_tw5iq6Xs

  34. https://medium.com/@ml.at.berkeley/tricking-neural-networks-create-your-own-adversarial-examples-a61eb7620fd8

  35. Exploit Software Dependencies • It takes advantage of the vulnerabilities

    of the software the system is connected to, not algorithms. • Prevention: – Security Scan – Security Reports – Be Careful : Wrappers ve Pre-Build Environment – Use Less Dependency – 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 – etc…
  36. Tool/Library Security (TensorFlow) • TensorFlow (tools like) is designed for

    internal communication, not for running on untrusted networks. • These tools (ModelServer etc.) do not have built-in authorization. • It can read and write files, send and receive data over the network… • (!) TensorFlow Models as Programs • (!) Running Untrusted Models • (!) Accepting Untrusted Inputs https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md
  37. https://www.tensorflow.org/tutorials/generative/adversarial_fgsm

  38. Cihan Özhan Links • cihanozhan.com • linkedin.com/in/cihanozhan • medium.com/@cihanozhan •

    youtube.com/user/OracleAdam • twitter.com/UnmannedCode • github.com/cihanozhan Contact • cihan@deeplab.co