Oracle international corporation (20240127630). DEEPFAKE DETECTION USING SYNCHRONOUS OBSERVATIONS OF MACHINE LEARNING RESIDUALS simplified abstract

DEEPFAKE DETECTION USING SYNCHRONOUS OBSERVATIONS OF MACHINE LEARNING RESIDUALS

Organization Name

oracle international corporation

Inventor(s)

Guy G. Michaeli of Seattle WA (US)

Mandip S. Bhuller of San Carlos CA (US)

Timothy D. Cline of Gainesville VA (US)

Kenny C. Gross of Escondido CA (US)

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

This abstract first appeared for US patent application 20240127630 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 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 can be applied in various fields such as:

• Media and entertainment industry for detecting fake news and manipulated videos
• Law enforcement for identifying forged evidence and false testimonies
• Online platforms for preventing the spread of misinformation and fake content

Problems Solved

This technology helps in addressing the following issues:

• Misinformation and fake news spreading rapidly through manipulated media
• Unauthorized use of someone's likeness for malicious purposes
• Ensuring the authenticity and credibility of audio-visual content in various applications

Benefits

The benefits of this technology include:

• Enhancing trust and credibility in digital media content
• Safeguarding individuals from identity theft and misuse of their images
• Improving the accuracy and reliability of audio-visual content analysis

Potential Commercial Applications

The technology can be commercially utilized in:

• Content moderation tools for social media platforms
• Security systems for detecting fraudulent activities
• Forensic analysis software for law enforcement agencies

Possible Prior Art

One possible prior art for this technology could be the use of machine learning algorithms for detecting anomalies in time series data, which has been applied in various fields such as finance and healthcare.

How does this technology handle real-time detection of deepfake content?

The patent application does not specify the real-time capabilities of the detection method. It would be interesting to know if the system can analyze and alert in real-time as new content is being generated.

What is the accuracy rate of this deepfake detection method compared to existing solutions?

The patent application does not provide information on the accuracy rate of the detection method. It would be valuable to understand how effective this technology is in comparison to other deepfake detection tools available in the market.

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.