Stealing Models from the Cloud

Data Skeptic · Beginner ·📐 ML Fundamentals ·9y ago

Key Takeaways

Florian Tramèr discusses reverse engineering machine learning models from cloud-based APIs, demonstrating the vulnerability of black box models in platform as a service data science applications, using techniques from his paper Stealing Machine Learning Models via Prediction APIs

Original Description

Platform as a service is a growing trend in data science where services like fraud analysis and face detection can be provided via APIs. Such services turn the actual model into a black box to the consumer. But can the model be reverse engineered? Florian Tramèr shares his work in this episode showing that it can. The paper Stealing Machine Learning Models via Prediction APIs is definitely worth your time to read if you enjoy this episode. Related source code can be found in https://github.com/ftramer/Steal-ML.
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Playlist

Uploads from Data Skeptic · Data Skeptic · 34 of 60

← Previous Next →
1 Data Skeptic book giveaway contest winner selection
Data Skeptic book giveaway contest winner selection
Data Skeptic
 2 OpenHouse - Front end and API overview
OpenHouse - Front end and API overview
Data Skeptic
 3 OpenHouse Crawling with AWS Lambda
OpenHouse Crawling with AWS Lambda
Data Skeptic
 4 [MINI] Logistic Regression on Audio Data
[MINI] Logistic Regression on Audio Data
Data Skeptic
 5 Data Provenance and Reproducibility with Pachyderm
Data Provenance and Reproducibility with Pachyderm
Data Skeptic
 6 [MINI] Primer on Deep Learning
[MINI] Primer on Deep Learning
Data Skeptic
 7 Big Data Tools and Trends
Big Data Tools and Trends
Data Skeptic
 8 [MINI] Automated Feature Engineering
[MINI] Automated Feature Engineering
Data Skeptic
 9 The Data Refuge Project
The Data Refuge Project
Data Skeptic
 10 [MINI] The Perceptron
[MINI] The Perceptron
Data Skeptic
 11 [MINI] Feed Forward Neural Networks
[MINI] Feed Forward Neural Networks
Data Skeptic
 12 Data Science at Patreon
Data Science at Patreon
Data Skeptic
 13 [MINI] Backpropagation
[MINI] Backpropagation
Data Skeptic
 14 [MINI] GPU CPU
[MINI] GPU CPU
Data Skeptic
 15 OpenHouse
OpenHouse
Data Skeptic
 16 [MINI] Generative Adversarial Networks
[MINI] Generative Adversarial Networks
Data Skeptic
 17 [MINI] AdaBoost
[MINI] AdaBoost
Data Skeptic
 18 [MINI] The Bootstrap
[MINI] The Bootstrap
Data Skeptic
 19 [MINI] Dropout
[MINI] Dropout
Data Skeptic
 20 [MINI] Gini Coefficients
[MINI] Gini Coefficients
Data Skeptic
 21 [MINI] Random Forest
[MINI] Random Forest
Data Skeptic
 22 [MINI] Heteroskedasticity
[MINI] Heteroskedasticity
Data Skeptic
 23 [MINI] ANOVA
[MINI] ANOVA
Data Skeptic
 24 Urban Congestion
Urban Congestion
Data Skeptic
 25 [MINI] The CAP Theorem
[MINI] The CAP Theorem
Data Skeptic
 26 Unstructured Data for Finance
Unstructured Data for Finance
Data Skeptic
 27 Detecting Terrorists with Facial Recognition?
Detecting Terrorists with Facial Recognition?
Data Skeptic
 28 Predictive Models on Random Data
Predictive Models on Random Data
Data Skeptic
 29 [MINI] Entropy
[MINI] Entropy
Data Skeptic
 30 [MINI] F1 Score
[MINI] F1 Score
Data Skeptic
 31 Causal Impact
Causal Impact
Data Skeptic
 32 Machine Learning on Images with Noisy Human-centric Labels
Machine Learning on Images with Noisy Human-centric Labels
Data Skeptic
 33 The Library Problem
The Library Problem
Data Skeptic
 ▶ Stealing Models from the Cloud
Stealing Models from the Cloud
Data Skeptic
 35 Data Science at eHarmony
Data Science at eHarmony
Data Skeptic
 36 Multiple Comparisons and Conversion Optimization
Multiple Comparisons and Conversion Optimization
Data Skeptic
 37 Election Predictions
Election Predictions
Data Skeptic
 38 [MINI] Calculating Feature Importance
[MINI] Calculating Feature Importance
Data Skeptic
 39 MS Connect Conference
MS Connect Conference
Data Skeptic
 40 Music21
Music21
Data Skeptic
 41 The Police Data and the Data Driven Justice Initiatives
The Police Data and the Data Driven Justice Initiatives
Data Skeptic
 42 Studying Competition and Gender Through Chess
Studying Competition and Gender Through Chess
Data Skeptic
 43 [MINI] Goodhart's Law
[MINI] Goodhart's Law
Data Skeptic
 44 Trusting Machine Learning Models with LIME
Trusting Machine Learning Models with LIME
Data Skeptic
 45 [MINI] Leakage
[MINI] Leakage
Data Skeptic
 46 Predictive Policing
Predictive Policing
Data Skeptic
 47 Mutli-Agent Diverse Generative Adversarial Networks
Mutli-Agent Diverse Generative Adversarial Networks
Data Skeptic
 48 [MINI] Convolutional Neural Networks
[MINI] Convolutional Neural Networks
Data Skeptic
 49 Unsupervised Depth Perception
Unsupervised Depth Perception
Data Skeptic
 50 [MINI] Max-pooling
[MINI] Max-pooling
Data Skeptic
 51 MS Build 2017
MS Build 2017
Data Skeptic
 52 Activation Functions
Activation Functions
Data Skeptic
 53 Doctor AI
Doctor AI
Data Skeptic
 54 [MINI] The Vanishing Gradient
[MINI] The Vanishing Gradient
Data Skeptic
 55 CosmosDB
CosmosDB
Data Skeptic
 56 Estimating Sheep Pain with Facial Recognition
Estimating Sheep Pain with Facial Recognition
Data Skeptic
 57 [MINI] Conditional Independence
[MINI] Conditional Independence
Data Skeptic
 58 MINI: Bayesian Belief Networks
MINI: Bayesian Belief Networks
Data Skeptic
 59 Project Common Voice
Project Common Voice
Data Skeptic
 60 [MINI] Recurrent Neural Networks
[MINI] Recurrent Neural Networks
Data Skeptic

This episode discusses the vulnerability of machine learning models in cloud-based APIs and demonstrates how to reverse engineer these models, highlighting the importance of API security and model protection

Key Takeaways
  1. Explore platform as a service data science applications
  2. Understand black box models and their vulnerabilities
  3. Read the paper Stealing Machine Learning Models via Prediction APIs
  4. Experiment with the related source code on https://github.com/ftramer/Steal-ML
  5. Apply reverse engineering techniques to machine learning models
💡 Machine learning models in cloud-based APIs can be reverse engineered, highlighting the need for API security and model protection

Related AI Lessons

⚡
Data Preprocessing: Encoding and Feature Scaling in Machine Learning
Learn to preprocess data by encoding and scaling features for better machine learning model performance
Medium · Machine Learning
 ⚡
Data Preprocessing: Encoding and Feature Scaling in Machine Learning
Learn to preprocess data for machine learning by encoding and scaling features, a crucial step for model training
Medium · Data Science
 ⚡
The Python Dictionary Trick That Makes Interviewers Smile
Learn the Python dictionary trick that impresses interviewers and improves your coding skills
Dev.to · Ameer Abdullah
 ⚡
I Compared 50 Python Courses. Here Are My Top 5 Recommendations for 2026
Discover the top 5 Python courses for 2026, curated from a comparison of 50 courses, to enhance your programming skills and career prospects
Medium · Python
Up next
Is Python Dead in 2026?| Truth About Python in AI Era | 90 Days Roadmap @FameWorldEducationalHub
FAME WORLD EDUCATIONAL HUB
Watch →