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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Related Experiment Video

Updated: Aug 13, 2025

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
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Deepfakes Generation and Detection: A Short Survey.

Zahid Akhtar1

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

Journal of Imaging
|January 20, 2023
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
DeepFakesbiometricsdeep learningdeepfake detectiondeepfake generationdigital face manipulationsdigital forensicsdisinformation face morphing attackface recognitionfake newsgenerative AIinformation authenticitymisinformationmultimedia manipulations

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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.
  • 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.