Making AI-Generated Content Traceable with a Hidden Code
Intel's patent describes a method for embedding a hidden, verifiable authentication code directly into AI-generated content, like deepfakes, during their creation process.
Original patent title: “Authenticator-integrated generative adversarial network (GAN) for secure deepfake generation”
Intel's patent describes a method for embedding a hidden, verifiable authentication code directly into AI-generated content, like deepfakes, during their creation process. Granted to Intel in 2025 with 23 claims, and it is expected to expire in 2041.
Coverage
What does this patent actually cover?
The patent describes a system that uses three interconnected AI networks: a generative neural network (the "creator"), a discriminator neural network (the "critic"), and an authenticator neural network (the "verifier"). The creator makes new samples, trying to fool the critic into thinking they are real. Crucially, the verifier also "digests" both real and created content, along with a secret "authentication code." The verifier then helps embed this code into the created samples by influencing the creator's training process (specifically, its "generator loss"). This results in AI-generated content that contains a hidden, verifiable authentication code, which the verifier can later use to confirm the content's origin. For example, a video generated by this system would have an invisible digital watermark proving it came from a specific source, verifiable by the authenticator neural network based on the embedded code.
The gap
What does this patent NOT cover?
- Does not cover methods for detecting deepfakes after they have been generated without an embedded authentication code.
- Does not cover authentication systems that rely on external watermarking or metadata added after content generation.
- Does not cover AI-generated content that does not involve a generative adversarial network (GAN) architecture.
- Does not cover authentication methods that do not involve an "authenticator neural network" directly contributing to the generator's training loss.
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 integrating the authenticator directly into the GAN's training loop. Instead of just trying to fool a discriminator, the generative network is also trained to embed a specific, verifiable authentication code, making the content inherently traceable from its origin.
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
Verifiable AI-generated news reports
Authentic AI-created product advertisements
Secure synthetic data for training other AI models
Digital watermarking for AI-generated art
Why it matters
The bigger picture
This technology addresses the growing challenge of distinguishing between real and AI-generated content, especially deepfakes. By embedding an authentication code during creation, it offers a way to verify the source of digital media. This could be crucial for combating misinformation and ensuring trust in digital content, particularly in news, social media, and legal contexts. It provides a mechanism for content creators to prove the authenticity and origin of their AI-generated works.
Filed
June 23, 2021
Granted
March 11, 2025
Market context
Who's building on this
Companies in this space
Intel, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a major player in AI hardware and software, and likely continues research in this area. Other companies like Google (DeepMind), Meta, Microsoft, and various startups are actively working on AI content generation, detection, and authenticity solutions. Adobe, for instance, has initiatives around content authenticity, focusing on content provenance and verifiable media.
Market impact
This patent addresses a critical emerging market need for trust and verification in AI-generated content. If widely adopted, it could establish a standard for responsible AI content creation, potentially leading to new tools for content provenance and combating misinformation. It could also create a competitive advantage for platforms that can guarantee the authenticity of their AI-generated media, influencing how digital content is produced and consumed across various industries.
Claim 1 — Plain English
What this patent covers
The patent describes a system that uses three interconnected AI networks: a generative neural network (the "creator"), a discriminator neural network (the "critic"), and an authenticator neural network (the "verifier"). The creator makes new samples, trying to fool the critic into thinking they are real. Crucially, the verifier also "digests" both real and created content, along with a secret "authentication code." The verifier then helps embed this code into the created samples by influencing the creator's training process (specifically, its "generator loss"). This results in AI-generated content that contains a hidden, verifiable authentication code, which the verifier can later use to confirm the content's origin. For example, a video generated by this system would have an invisible digital watermark proving it came from a specific source, verifiable by the authenticator neural network based on the embedded code.
The clever bit
The novelty lies in integrating the authenticator directly into the GAN's training loop. Instead of just trying to fool a discriminator, the generative network is also trained to embed a specific, verifiable authentication code, making the content inherently traceable from its origin.
What it does not cover
- Does not cover methods for detecting deepfakes after they have been generated without an embedded authentication code.
- Does not cover authentication systems that rely on external watermarking or metadata added after content generation.
- Does not cover AI-generated content that does not involve a generative adversarial network (GAN) architecture.
- Does not cover authentication methods that do not involve an "authenticator neural network" directly contributing to the generator's training loss.
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
0/40
No citations yet
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
20/20
Major company or institution
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
$37K – $120K
Midpoint $75K · 14.7 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
Demir, I., Marshall, C. S., Srivastava, S., & Gans, S. (2025). Making AI-Generated Content Traceable with a Hidden Code (U.S. Patent No. 12,248,556). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12248556/authenticator-integrated-generative-adversarial-network-gan-for-secure-deepfake-
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 Making AI-Generated Content Traceable with a Hidden Code cover?
Intel's patent describes a method for embedding a hidden, verifiable authentication code directly into AI-generated content, like deepfakes, during their creation process.
Who owns patent US 12248556?
Intel owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on June 23, 2041, when the invention enters the public domain.
What problem does this patent solve?
This technology addresses the growing challenge of distinguishing between real and AI-generated content, especially deepfakes. By embedding an authentication code during creation, it offers a way to verify the source of digital media. This could be crucial for combating misinformation and ensuring trust in digital content, particularly in news, social media, and legal contexts. It provides a mechanism for content creators to prove the authenticity and origin of their AI-generated works.
What does this patent NOT cover?
Does not cover methods for detecting deepfakes after they have been generated without an embedded authentication code.
Same assignee
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