Smart Device Deepfake Verification Using Hidden JWT Hashes

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Solution Overview

Problem

The proliferation of deep fake content, particularly in videos and images, poses a challenge as it is difficult to distinguish between real and fake content, leading to potential misinformation and safety risks, especially in contexts like political campaigns and autonomous vehicles.

Innovation Solution

Implementing a system that embeds a hidden object, such as a JSON Web Token (JWT) using Least Significant Bit (LSB) steganography, within content to verify its legitimacy, involving smart devices like smart televisions and vehicles, which analyze the content for authenticity by comparing hashes and signatures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If deep learning techniques are used to generate deep fake content, then the realism and indetectability of fake content is improved, but the ability to distinguish between real and fake content deteriorates

Engineering Contradiction:

Improverealism of deep fake contentVSAvoiddetection difficulty of deep fake content

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent introduces verification tokens as intermediary elements embedded within deep fake content. These tokens act as mediators between the fake content and the verification system, allowing authentication without altering the visual appearance of the content itself. The token serves as a hidden marker that can be detected by verification systems while remaining imperceptible to human viewers.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual visual inspection with automated verification systems that use cryptographic hashing and token validation mechanisms. Instead of relying on human observers to detect subtle artifacts in deep fake content, the system uses computational methods to verify authenticity through embedded verification tokens, substituting mechanical visual analysis with digital authentication protocols.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If verification tokens are embedded in content using LSB steganography, then the authenticity verification capability is improved, but the content processing complexity increases

Engineering Contradiction:

Improveauthenticity verification capabilityVSAvoidcontent processing complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies LSB steganography selectively to specific regions and layers of the content rather than processing the entire content uniformly. The verification tokens are embedded only in the least significant bits where they do not affect the perceived quality of the content, allowing verification functionality to be added with minimal impact on overall content processing complexity.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The verification tokens and their corresponding hashes are embedded and prepared in advance during the content creation or processing stage. This preliminary action allows the verification system to operate more efficiently later, as the authentication data is already in place and does not require additional processing during playback or consumption of the content.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If hash comparison and signature verification are performed, then the detection accuracy of deep fake content is improved, but the processing time increases

Engineering Contradiction:

Improvedetection accuracy of deep fake contentVSAvoidprocessing time for verification

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs verification by extracting and validating only the critical verification tokens and their hashes rather than analyzing every pixel and data element of the content. This partial verification approach maintains high detection accuracy by focusing computational resources on the authentication markers while significantly reducing the overall processing time compared to complete content analysis.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system creates and validates cryptographic copies of verification tokens rather than analyzing the original content repeatedly. By working with hashed representations and digital signatures of the verification data, the system can perform multiple verification checks without re-processing the entire content, thereby improving detection accuracy while minimizing processing time through efficient data replication and comparison.

Inventive Principle:

Principle #26Copying

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables real-time verification of content authenticity, preventing the dissemination of deep fake content and ensuring the safety of autonomous vehicles by validating the source and integrity of media.

Implementation Method 1

The JWT can be embedded in the content using Least Significant Bit (LSB) steganography

Methodology Applied

Scientific EffectLeast Significant Bit (LSB) steganography:

Data Source

PatentUS12483415B2Identifying deep fake content via smart devices

Publication Date: 2025.11.25 DIGICERT INC

AI summary

A method of identifying deep fake content includes receiving content at a smart device; determining whether the content includes a hidden object therein; responsive the content including the hidden object, determining a hash in the hidden object; determining a local hash for the content by the smart device; and determining legitimacy of the content based on the hash in the hidden object and the local hash. The hidden object can be a JavaScript Object Notation (JSON) Web Token (JWT).