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Deepfakes Generation and Detection: A Short Survey.

¹Department of Network and Computer Security, State University of New York (SUNY) Polytechnic Institute, Utica, NY 13502, USA.

Journal of Imaging

|January 20, 2023

Summary

This summary is machine-generated.

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DeepFakes, realistic synthetic media created with deep learning, pose risks to face recognition systems. This survey details generation and detection methods, highlighting an ongoing arms race between creators and detectors.

Area of Science:

Computer Vision
Artificial Intelligence
Multimedia Forensics

Background:

Deep learning advancements enable realistic facial manipulation and generation.
Accessible tools facilitate both benign and malicious use of synthetic media.
Face recognition systems exhibit vulnerabilities to various face manipulations.

Purpose of the Study:

To survey techniques for DeepFake generation and manipulation.
To review methods for detecting DeepFakes and manipulated faces.
To identify open challenges and future research directions in the field.

Main Methods:

Review of recent literature on DeepFake generation techniques.
Categorization of manipulations into identity swap, face reenactment, attribute manipulation, and entire face synthesis.

Keywords:

DeepFakes biometrics deep learning deepfake detection deepfake generation digital face manipulations digital forensics disinformation face morphing attack face recognition fake news generative AI information authenticity misinformation multimedia manipulations

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Analysis of both generation and detection methods for each manipulation type.

Main Results:

Detailed overview of four key DeepFake manipulation categories and their associated generation methods.
Examination of corresponding detection techniques, highlighting progress and limitations.
Identification of a continuous escalation between DeepFake generation and detection capabilities.

Conclusions:

Significant progress in DeepFake generation and detection exists.
An ongoing arms race between adversaries and defenders necessitates further research.
Future directions include developing more robust detection methods and understanding the evolving landscape of synthetic media.