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Related Experiment Video

Updated: Aug 17, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Depth map guided triplet network for deepfake face detection.

Buyun Liang¹, Zhongyuan Wang¹, Baojin Huang¹

¹School of Computer Science, Wuhan University, Wuhan, China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 17, 2022

Summary

This summary is machine-generated.

This study introduces a novel depth map guided triplet network for improved deepfake detection. The method effectively distinguishes real from fake faces by analyzing depth inconsistencies, outperforming existing approaches.

Keywords:

Deepfake detection Depth map Triplet network

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Area of Science:

Computer Vision
Artificial Intelligence
Digital Forensics

Background:

Deepfake technology poses ethical challenges due to its widespread use.
Current deepfake detection methods often treat the task as fine-grained classification, limiting feature extractor capabilities for real vs. fake attributes.

Purpose of the Study:

To propose a novel deepfake detection method using a depth map guided triplet network.
To enhance the extraction of discriminative features for distinguishing real and fake faces.

Main Methods:

A depth prediction network generates depth maps highlighting real/fake face differences (discontinuity, illumination, blurring).
A triplet feature extraction network employs triplet loss to ensure real faces are close in latent space and fake faces are distant.

Main Results:

The proposed method effectively leverages depth map information for deepfake detection.
Experiments on FaceForensics++ and Celeb-DF datasets demonstrate superior performance compared to existing methods.

Conclusions:

The depth map guided triplet network offers a more effective approach to deepfake detection.
This method provides a robust way to identify manipulated facial imagery by focusing on latent feature space distinctions.