Extract AI Model
Model extraction is like a student secretly copying a teacher's entire answer key by asking them thousands of specific questions and recording the results. Once the student has enough answers, they can create their own version of the test that performs just as well as the original, without needing to do the actual work or pay for access to the teacher.
Model extraction is an adversarial technique where an attacker treats a target model as a black box, repeatedly querying its API to build a labeled dataset of inputs and outputs. This dataset is then used to train a local surrogate model that approximates the decision boundaries of the original, allowing the attacker to bypass access controls, avoid inference costs, or steal proprietary intellectual property.
Model extraction is a class of adversarial machine learning attacks where an adversary reconstructs a functional approximation of a victim model by leveraging its inference API. By systematically querying the target with a distribution of inputs and observing the corresponding outputs, the adversary constructs a training set to perform knowledge distillation. This process enables the creation of a high-fidelity surrogate model that replicates the target's predictive behavior, facilitating intellectual property theft and the circumvention of AI-as-a-Service (AIaaS) monetization models.