# How to Check if an AI Model Has Been Tampered With > This patent describes a method to verify the integrity and authorized use of an artificial intelligence model by feeding it a special digital key and comparing its response to a known good one. - **Patent:** US 11544411 - **Original title:** Machine learning model validation and authentication - **Owner:** Koninklijke Philips NV - **Granted:** 2023 - **Status:** Active - **Times cited:** 1 - **Field:** software, ai_ml, telecommunications, consumer_electronics ## What it does The patent outlines a method for validating and authenticating machine learning models. It works by first "providing a digital key that is associated with a particular entity" (Claim 1). This key is then "applied as input across at least a portion of the trained machine learning model to generate one or more verification outputs" (Claim 1). These outputs are then "compared to one or more known verification outputs" that were previously generated when the model was known to be uncompromised (Claim 1). If the comparison reveals a discrepancy, the system "determining... that one or more parameters of the trained machine learning model have been compromised" and then indicates this compromise (Claim 1). For example, a company providing an AI model for medical diagnosis could periodically feed it a unique digital key. If the model's response to this key changes from the expected output, it signals that the model might have been altered or misused. ## What it does NOT cover - Does not cover detecting model compromise without using a specific digital key as input to the model itself. - Does not cover validation methods that only check the final output without comparing it to a known output generated by a prior application of the same key. - Does not cover general cybersecurity measures for AI models that do not involve this specific input-output verification process. - Does not cover methods that do not involve comparing outputs to *known* outputs generated by *prior* application of the digital key. ## The clever bit The novelty lies in treating the machine learning model itself as a system that can be 'fingerprinted' by its response to a unique 'digital key.' Instead of just using a key for access, it's used as an input to the model, and the model's internal or external behavior in response is checked against a known baseline to detect any changes. ## Real-world examples 1. Software for detecting tampering in medical AI diagnostics platforms. 2. Systems for verifying the integrity of financial fraud detection models. 3. Platforms ensuring licensed use and preventing unauthorized copying of proprietary AI models. 4. Automated checks for AI models embedded in industrial control systems. ## Why it matters Machine learning models are increasingly deployed in critical sectors like healthcare, finance, and autonomous systems. Ensuring their integrity and preventing unauthorized use or malicious tampering is vital for trust, security, and regulatory compliance. This patent offers a specific mechanism to detect if a deployed model has been altered, which could otherwise lead to incorrect predictions, biased outcomes, or security vulnerabilities. ## Frequently asked questions ### What does How to Check if an AI Model Has Been Tampered With cover? This patent describes a method to verify the integrity and authorized use of an artificial intelligence model by feeding it a special digital key and comparing its response to a known good one. ### Who owns patent US 11544411? Koninklijke Philips NV owns this patent, granted in 2023. ### When does this patent expire? This patent is expected to expire on January 14, 2040, when the invention enters the public domain. ### What is patent US 11544411 cited by? This patent has been cited by 1 later patents that build on its ideas. ### What problem does this patent solve? Machine learning models are increasingly deployed in critical sectors like healthcare, finance, and autonomous systems. Ensuring their integrity and preventing unauthorized use or malicious tampering is vital for trust, security, and regulatory compliance. This patent offers a specific mechanism to detect if a deployed model has been altered, which could otherwise lead to incorrect predictions, biased outcomes, or security vulnerabilities. ### What does this patent NOT cover? Does not cover detecting model compromise without using a specific digital key as input to the model itself. **Full plain-English explainer:** https://patentbrief.org/patent/us/11544411/machine-learning-model-validation-and-authentication **Original patent:** https://patents.google.com/patent/US11544411 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How to Automatically Detect and Fix Changes in AI Model Data](https://patentbrief.org/patent/us/10599957/systems-and-methods-for-detecting-data-drift-for-data-used-in-machine-learning-m) — This patent describes a system that automatically notices when the real-world data an AI model sees changes, causing its predictions to become less accurate, and then fixes the model. - [How to Force AI to Follow Logical Rules During Training](https://patentbrief.org/patent/us/11651227/muzero) — A system that uses a dual-headed neural network to ensure AI models obey specific logical rules by embedding those rules directly into the training process. - [Improving AI Predictions by Factoring in How Much You Trust Each Model](https://patentbrief.org/patent/us/20230044102/ensemble-machine-learning-models-incorporating-a-model-trust-factor) — This patent describes a method for making an AI system more accurate by combining several individual AI models and giving more weight to the ones considered more trustworthy. - [Training AI on Private Data Without Seeing It](https://patentbrief.org/patent/us/12518214/distributed-machine-learning-systems-including-generation-of-synthetic-data) — This patent describes a way to train artificial intelligence models using private data stored on many separate computers, by generating fake data that mimics the real data's patterns, so the private data itself never leaves its original location. - [How Devices Train Shared AI Models While Keeping Your Data Private](https://patentbrief.org/patent/us/12443890/partially-local-federated-learning) — This patent describes a method for training a machine learning model across many devices, where each device keeps some parts of the model and its data private, only sharing updates for the common, global parts of the model.