DEEPFAKE DETECTION USING SYNCHRONOUS OBSERVATIONS OF MACHINE LEARNING RESIDUALS

Organization Name

Inventor(s)

DEEPFAKE DETECTION USING SYNCHRONOUS OBSERVATIONS OF MACHINE LEARNING RESIDUALS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17967254 titled 'DEEPFAKE DETECTION USING SYNCHRONOUS OBSERVATIONS OF MACHINE LEARNING RESIDUALS

Simplified Explanation

The patent application describes a method for detecting deepfake content in audio-visual content by analyzing time series signals and detecting anomalies in the residual values.

Converting audio-visual content into time series signals
Generating residual time series signals to compare with machine learning estimates of authentic delivery
Placing residual values into an array for sequential analysis
Detecting anomalies in the residual values to identify deepfake content
Generating an alert when deepfake content is detected

Potential Applications

This technology could be used in:

Media and entertainment industry for detecting deepfake videos
Law enforcement for identifying manipulated evidence
Online platforms for content moderation and fake news detection

Problems Solved

Detection of deepfake content in audio-visual media
Prevention of misinformation and manipulation through fake videos
Protection against malicious use of deepfake technology

Benefits

Enhances security and authenticity of audio-visual content
Helps in maintaining trust and credibility in media and information sources
Enables quick and accurate identification of deepfake content

Potential Commercial Applications

Deepfake detection software for media companies
Integration into social media platforms for content moderation
Licensing the technology to law enforcement agencies for forensic analysis

Possible Prior Art

One possible prior art in this field is the use of machine learning algorithms for deepfake detection in audio-visual content. Researchers have developed various techniques to identify manipulated media using AI and signal processing methods.

What are the limitations of this technology in detecting deepfake content?

The technology may have limitations in detecting highly sophisticated deepfake content that closely mimics authentic audio-visual signals. It may also struggle with identifying deepfake content that has been specifically designed to evade detection algorithms.

How does this technology compare to existing deepfake detection methods?

This technology offers a sequential analysis approach to detecting anomalies in residual values, which may provide a more robust and accurate method for identifying deepfake content compared to traditional deepfake detection techniques.

Original Abstract Submitted

Systems, methods, and other embodiments associated with computer deepfake detection are described. In one embodiment, a method includes converting audio-visual content of a person delivering a speech into a set of time series signals. Residual time series signals of residuals that indicate an extent to which the time series signals differ from machine learning estimates of authentic delivery of the speech by the person are generated. Residual values from one synchronous observation of the residual time series signals are placed into an array of residual values for a point in time. A sequential analysis of the residual values of the array is performed to detect an anomaly in the residual values for the point in time. In response to detection of the anomaly, an alert that deepfake content is detected in the audio-visual content is generated.

17967254. DEEPFAKE DETECTION USING SYNCHRONOUS OBSERVATIONS OF MACHINE LEARNING RESIDUALS simplified abstract (Oracle International Corporation)

Contents

DEEPFAKE DETECTION USING SYNCHRONOUS OBSERVATIONS OF MACHINE LEARNING RESIDUALS

Organization Name

Inventor(s)