From 21843c1ba359bfbe5d2278757e7caf9a974fcd34 Mon Sep 17 00:00:00 2001 From: Matteo Meucci Date: Thu, 20 Nov 2025 22:45:51 +0100 Subject: [PATCH] Update AITG-APP-09_Testing_for_Model_Extraction.md --- .../tests/AITG-APP-09_Testing_for_Model_Extraction.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/Document/content/tests/AITG-APP-09_Testing_for_Model_Extraction.md b/Document/content/tests/AITG-APP-09_Testing_for_Model_Extraction.md index 3819aac..ca56e0c 100644 --- a/Document/content/tests/AITG-APP-09_Testing_for_Model_Extraction.md +++ b/Document/content/tests/AITG-APP-09_Testing_for_Model_Extraction.md @@ -12,19 +12,19 @@ Model extraction attacks involve adversaries systematically querying a machine l **Payload 1:** - Systematic querying of the model using adaptive query strategies (black-box extraction). - - **Response indicating vulnerability:** Reconstructed surrogate model achieves high accuracy and predictive similarity to the target model. +Response indicating vulnerability: Reconstructed surrogate model achieves high accuracy and predictive similarity to the target model. **Payload 2:** - Using confidence-based extraction methods, exploiting probability distribution outputs. - - **Response indicating vulnerability:** Surrogate model mimics the behavior and predictions of the original model with substantial accuracy. +Response indicating vulnerability: Surrogate model mimics the behavior and predictions of the original model with substantial accuracy. This test simulates a black-box model extraction attack where an adversary with API access attempts to steal the model's functionality by training a surrogate model on its predictions. #### Prerequisites -- **API Access**: An API endpoint for the target model that accepts input data and returns predictions. -- **Dataset**: A representative dataset for querying the model. For a classification model, this would be a set of inputs (e.g., text samples, images). The dataset should be split into a training set (for querying) and a test set (for evaluation). -- **Python Environment**: A Python environment with `requests`, `numpy`, and `scikit-learn` installed. +- API Access: An API endpoint for the target model that accepts input data and returns predictions. +- Dataset: A representative dataset for querying the model. For a classification model, this would be a set of inputs (e.g., text samples, images). The dataset should be split into a training set (for querying) and a test set (for evaluation). +- Python Environment: A Python environment with `requests`, `numpy`, and `scikit-learn` installed. #### Step 1: Data Acquisition via API Queries