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US 2026/0073732 A1Published application
System and method for real-time detection of deepfakes
AssigneeEnsign InfoSecurity Pte. Ltd.
InventorsJoon Sern Lee, Divakar Sivashankar, Ting Yew Koh, Karthik Ravikanti, Aratrika Pal
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Abstract
This document describes a system and method for detecting deepfakes in real-time. In particular, the described system and method is configured to detect, in real-time, if a captured image and/or audio segment comprises a deepfake
Claims20
A computing module for real-time detection of deepfakes comprising:
- a processing unit;
- and
- a non-transitory media readable by the processing unit, the media storing instructions that when executed by the processing unit causes the processing unit to:
- acquire an image and extract at least one facial image from the image;
- apply a trained deepfake visual detection model to the at least one extracted facial image, the visual detection model performing:
- a binary classification of the at least one facial image to determine if the at least one facial image comprises manipulations;
- a per-pixel classification of the at least one facial image to generate a mask indicating regions of potential manipulation;
- determine if the acquired image comprises a deepfake image based on the binary and the per-pixel classifications of the at least one facial image;
- acquire an audio segment and pre-process the audio segment;
- reconstruct original audio characteristics of the pre-processed audio segment using a trained audio inverter model;
- apply a trained deepfake audio detection model to the reconstructed original audio characteristics, the audio detection model performing classification of the reconstructed original audio characteristics;
- determine if the acquired audio segment comprises a deepfake audio based on the classification of the reconstructed original audio characteristics.
9 more elements
A method for real-time detection of deepfakes using a computing module, the method comprising the steps of:
- acquiring an image and extracting at least one facial image from the image;
- applying a trained deepfake visual detection model to the at least one extracted facial image, the visual detection model performing:
- a binary classification of the at least one facial image to determine if the at least one facial image comprises manipulations;
- a per-pixel classification of the at least one facial image to generate a mask indicating regions of potential manipulation;
- determining if the acquired image comprises a deepfake image based on the binary and the per-pixel classifications of the at least one facial image;
- acquiring an audio segment and pre-process the audio segment;
- reconstructing original audio characteristics of the pre-processed audio segment using a trained audio inverter model;
- applying a trained deepfake audio detection model to the reconstructed original audio characteristics, the audio detection model performing classification of the reconstructed original audio characteristics;
- determining if the acquired audio segment comprises a deepfake audio based on the classification of the reconstructed original audio characteristics.
6 more elements
Dependencies are inferred from the claim text.