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A destructive active defense algorithm for deepfake face images.

Yang Yang¹, Norisma Binti Idris¹, Chang Liu²

¹Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.

Peerj. Computer Science

|December 16, 2024

Summary

This summary is machine-generated.

A new destructive active defense algorithm for deepfake face images (DADFI) creates subtle image changes. This method enhances defense capabilities against deepfakes by distorting deepfake model outputs, reducing harm from malicious content.

Keywords:

Active defense Adversarial samples Deep fake Face images

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Area of Science:

Computer Science
Artificial Intelligence
Cybersecurity

Background:

Deepfake face images pose increasing harm and security risks.
Proactive defense mechanisms are crucial to combat sophisticated deepfake generation techniques.

Purpose of the Study:

To introduce a novel destructive active defense algorithm for deepfake face images (DADFI).
To develop a method for generating adversarial samples that disrupt deepfake models while maintaining image authenticity.

Main Methods:

The DADFI algorithm introduces imperceptible perturbations to original face images, creating adversarial samples.
Adversarial samples are utilized in black-box scenarios to attack and probe deepfake models.
Experiments were conducted on CASIA-FaceV5 and CelebA datasets.

Main Results:

The DADFI algorithm successfully generates adversarial samples with high visual fidelity and authenticity.
The method demonstrated improved generation speed for adversarial samples.
A significant increase in the success rate of active defense against deepfake models was observed.

Conclusions:

The proposed DADFI algorithm offers an effective active defense strategy against deepfake face images.
This approach can mitigate the escalating harm caused by malicious deepfake content.
DADFI enhances both the speed and efficacy of deepfake detection and defense systems.