Deepfake Attack Detection Through Adaptive Media Capture
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Solution Overview
Problem
Existing methods for verifying the authenticity of digital media, such as photos and videos, are inadequate in preventing manipulation and tampering, particularly against advanced techniques like deepfakes, as they rely on untrustworthy assumptions about user behavior and are computationally intensive.
Innovation Solution
A client-server system that instructs the capturing device to implement specific operation modes, such as altering frame rate, light sensitivity, or generating audio signals, and verifies compliance in real-time to ensure media authenticity, using a server that checks for these changes in captured media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing verification methods are used to check media authenticity, then verification can be performed, but the computational burden on the server becomes excessive and the system becomes vulnerable to deepfake attacks
Solution Approach 1:
The system performs preliminary actions by having the capturing device generate operation mode logs and compliance data during the media capture process itself. This shifts the computational burden from the server to the capturing device, allowing the server to only verify pre-computed compliance data rather than performing intensive analysis on the actual media content.
Solution Approach 2:
The patent introduces an intermediary verification mechanism where the capturing device acts as a mediator between media creation and server verification. The device generates operation mode logs that serve as intermediate evidence, allowing the server to verify authenticity without directly analyzing the media content, thus reducing computational burden.
2Reliability
If existing verification methods are used to check media authenticity, then verification can be performed, but the system becomes vulnerable to manipulation and tampering
Solution Approach 1:
The system implements feedback by continuously monitoring operation mode compliance during media capture and generating real-time compliance data. This feedback mechanism allows the system to detect deviations from expected capture patterns that would indicate manipulation or deepfake attacks, enhancing verification reliability.
Solution Approach 2:
The capturing device performs preliminary verification by generating operation mode logs during capture. This preliminary action creates a trusted record of capture parameters before the media can be manipulated, making it difficult for deepfake attacks to succeed since the compliance data is generated at the source rather than verified later on potentially tampered content.
3Reliability
If the capturing device implements multiple operation mode changes during media capture, then media authenticity verification becomes more reliable, but the complexity of the capturing device increases
Solution Approach 1:
The system uses parameter changes by varying operation modes (such as camera settings, microphone gain, flash intensity) during media capture. These parameter changes create distinctive patterns in the operation mode logs that verify authenticity. The capturing device complexity increases only minimally since it uses existing hardware capabilities in varied sequences rather than adding new components.
Data Source
AI summary
A method, computer program product and a system for video authentication comprising: a capturing-device configured to continuously capture media, a real-time instruction module configured to provide instructions to the capturing-device causing it to change, in real-time, an operation mode while capturing a media segment, a signing module configured to create, in real-time, signature for the media segments and transmit the to a storage module retaining pairs of a signature of a media segment, and respective instructions that were provided to the capturing-device while capturing the media segment. The media authenticity verification module receives a media segment to determine authenticity of the media segment by obtaining a respective pair of a signature and instructions; utilizing the signature to ensure that the media segment was not manipulated after being captured; and verifying that the media segment complies with the instructions, whereby protecting against potential deepfake attacks.