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Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

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MagNet: Detecting Digital Presentation Attacks on Face Recognition.

Akshay Agarwal¹, Richa Singh², Mayank Vatsa²

¹Indraprastha Institute of Information Technology Delhi, New Delhi, India.

Frontiers in Artificial Intelligence

|December 27, 2021

Summary

This summary is machine-generated.

This study introduces MagNet, a novel algorithm for detecting digital presentation attacks on face recognition systems. MagNet utilizes a Weighted Local Magnitude Pattern (WLMP) feature descriptor for enhanced security against image manipulation.

Keywords:

DeepFake detection digital threats face morphing attack face recognition (FR)face swapping

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Area of Science:

Computer Science
Biometrics
Cybersecurity

Background:

Face recognition systems face threats from physical and digital presentation attacks.
Digital attacks like morphing and deepfakes are increasingly sophisticated due to advancements in AI and computer vision.
Limited research has focused on detecting these digital attacks, posing a significant security risk.

Purpose of the Study:

To develop a novel algorithm for detecting digital presentation attacks on face recognition systems.
To introduce a new database for evaluating digital presentation attack detection methods.
To address the growing security concerns posed by AI-driven image manipulation.

Main Methods:

Proposed MagNet algorithm using a Weighted Local Magnitude Pattern (WLMP) feature descriptor.
Created the "I-nder" database with swapping/morphing and neural face transformation subsets, using social media platforms.
Evaluated performance on multiple datasets including the proposed database, FaceForensic, GAN images, and real-world data.

Main Results:

MagNet demonstrated effective performance in detecting digital presentation attacks.
The proposed algorithm achieved lower error rates compared to existing methods.
MagNet offers computational efficiency, making it practical for real-world applications.

Conclusions:

MagNet presents a promising solution for digital presentation attack detection in face recognition systems.
The "I-nder" database provides a valuable resource for future research in this domain.
The algorithm's effectiveness and efficiency highlight its potential to enhance biometric security.