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.
Original patent title: “Machine learning model validation and authentication”
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. Granted to Koninklijke Philips NV in 2023 with 23 claims and 1 forward citation, and it is expected to expire in 2040.
Coverage
What does this patent actually cover?
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" (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 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.
The gap
What does this patent 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.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → 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.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Software for detecting tampering in medical AI diagnostics platforms.
Systems for verifying the integrity of financial fraud detection models.
Platforms ensuring licensed use and preventing unauthorized copying of proprietary AI models.
Automated checks for AI models embedded in industrial control systems.
Why it matters
The bigger picture
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.
Filed
January 14, 2020
Granted
January 3, 2023
Market context
Who's building on this
Companies in this space
Companies like Microsoft, Google, and IBM, which develop and deploy large-scale AI models, are actively working on solutions for AI security and integrity. Philips, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, continues to develop AI solutions, particularly in healthcare. A growing number of startups focused on MLOps (Machine Learning Operations) and AI trust and safety are also active in this space, building tools to monitor and secure AI deployments.
Market impact
This type of technology contributes to building trust in AI systems, which is essential for their broader adoption, especially in highly regulated industries. It provides a mechanism for model owners to enforce licensing agreements and detect intellectual property theft or unauthorized alterations, potentially leading to more secure, auditable, and commercially viable AI deployments. It helps address a critical need for governance in the rapidly expanding AI market.
Claim 1 — Plain English
What this patent covers
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.
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.
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.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
6/40
Early citations
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$75K – $240K
Midpoint $150K · 13.4 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Stapleton, S. A. P., & Maraghoosh, A. M. T. (2023). How to Check if an AI Model Has Been Tampered With (U.S. Patent No. 11,544,411). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/11544411/machine-learning-model-validation-and-authentication
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
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.
Same assignee
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