DeepFake Detection Algorithms: A Meta-Analysis
Abstract
We analyzed the developed methods of computer vision in areas associated with recognition and detection of DeepFakes using various models and architectures of neural networks: mainly GAN and CNN. We also discussed the main types and models of these networks that are most effective in detecting and recognizing objects from different data sets, which were provided in the studied articles.
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Deepfakes are by far the most prevalent form of synthetic media.
“Among these various forms, deepfakes are by far the most prevalent (Chesney & Citron, 2019; Zotov et al., 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Zotov et al., 2020
Most deepfake detection methods are supervised and have difficulty generalising across domains and datasets.
“Most of these detection methods are supervised, and they have difficulty generalising across domains and datasets (Zotov etal. 2020).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Zotov et al. 2020
Deepfake technologies pose a significant threat to biometric authentication technologies, potentially disrupting businesses providing authentication services.
“Similarly, deepfake technologies pose a significant threat to biometric authentication technologies, potentially disrupting businesses providing authentication services (Botha & Pieterse, 2020; Schwartz, 2018; Zotov et al., 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Zotov et al., 2020
Addressing the surge of algorithm-generated misinformation opens up a new field of business for developing AI-based solutions and services that detect synthetic content from human-generated content and provide consumers with warnings when confronted with marketplace deception or suspicious content.
“Addressing the surge of algorithm-generated misinformation opens up a new field of business for developing AI-based solutions and services that detect synthetic content from human-generated content and provide consumers with warnings when confronted with marketplace deception or suspicious content (Maksutov et al., 2020; Torres et al., 2018; Zotov et al., 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Zotov et al., 2020
The current knowledge on deepfakes is scant and diffuse.
“The current knowledge on deepfakes is scant and diffuse (Maksutov et al., 2020; Zotov et al., 2020).”
From Deepfakes: Deceptions, mitigations, and opportunities (2023) · cites this paper as Zotov et al., 2020
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AI-Driven Identity Manipulation
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- Authors
- Zotov, S., Dremliuga, R., Borshevnikov, A., & Krivosheeva, K.
- Affiliations
- Far Eastern Federal University (RU)
- Far eastern federal university, Russia
- ORCID
- Zotov, Sergey
- Venue
- 2020 2nd Symposium on Signal Processing Systems
- Publisher
- ACM
- Published
- 2020-07-11
- Pages
- 43-48
- OpenAlex
- W3111214174
- Cited by
- 12
- References
- 20
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 2 · 2022: 2 · 2021: 1
- FWCI
- 0.39
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Symposium on Signal Processing Systems (SSPS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- deepfake detection, generative adversarial networks, convolutional neural networks, neural network architecture, computer vision
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