Digital Content Authentication via Intrinsic Attribute Hashing
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Solution Overview
Problem
The increasing sophistication of deepfake technology makes it difficult to detect and authenticate digital content, leading to potential misuse and legal risks for content creators and distributors.
Innovation Solution
A system and method for authenticating digital content based on its intrinsic attributes, which involves comparing validation data to authentication data created from the original or baseline version of the content, using the same cryptographic technique to ensure robustness and foolproof accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning solutions are used to detect deepfakes, then detection capability is improved, but detection accuracy deteriorates as deepfake technology improves
Solution Approach 1:
The system creates a baseline version of the digital content and generates authentication data from it before any manipulation occurs. This preliminary authentication data serves as a reference for future verification, enabling the system to detect deepfakes by comparing against this pre-established baseline rather than attempting to detect manipulation in real-time.
Solution Approach 2:
The system creates a cryptographic copy (hash) of the baseline content that serves as authentication data. This copy is not the actual content but a derived representation that can be compared to verify authenticity, allowing the system to authenticate content without needing to analyze the full complexity of the original digital content each time.
2Reliability
If cryptographic techniques are used to create authentication data, then authentication robustness is improved, but processing complexity increases
Solution Approach 1:
The system replaces complex manual or mechanical authentication processes with cryptographic hash functions. Instead of using elaborate verification procedures, the system uses mathematical hashing to create and compare authentication data, providing robust security through well-established cryptographic principles rather than complex procedural mechanisms.
3Productivity
If automated authentication processes are implemented, then productivity is improved, but system complexity increases
Solution Approach 1:
The system performs authentication automatically by comparing the hash of received content against the stored baseline hash without requiring manual intervention. The process is self-service in that the system handles the entire authentication workflow autonomously, from generating the baseline hash to verifying incoming content, thereby improving productivity while keeping the operational complexity manageable.
Data Source
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AI summary
A system for performing authentication of content based on intrinsic attributes includes a computing platform having a hardware processor and a memory storing a content authentication software code. The hardware processor executes the content authentication software code to receive a content file including digital content and authentication data created based on a baseline version of the digital content, to generate validation data based on the digital content, to compare the validation data to the authentication data, and to identify the digital content as baseline digital content in response to determining that the validation data matches the authentication data based on the comparison. The hardware processor is also configured to execute the content authentication software code to identify the digital content as manipulated digital content in response to determining that the validation data does not match the authentication data based on the comparison.