Unmasking digital deceptions: An integrative review of deepfake detection, multimedia forensics, and cybersecurity challenges
Open accessAbstract
• Presents an in-depth review of deepfake generation and detection, highlighting AI methods such as GANs, face synthesis, and speech cloning. • Evaluates critically the weaknesses of biometric systems and the difficulties of cross-dataset testing for deepfake detection. • Suggest interdisciplinary solutions—such as explainable AI, federated learning, and policy frameworks—to counteract the social and cybersecurity implications of deepfakes. Deepfakes, which are driven by developments in generative AI, seriously jeopardize public trust, cybersecurity, and the veracity of information. This study offers a comprehensive analysis of the most recent methods for creating and detecting deepfakes in image, video, and audio modalities. With a focus on their advantages and disadvantages in cross-dataset and real-world scenarios, we compile the latest developments in transformer-based detection models, multimodal biometric defenses, and Generative Adversarial Networks (GANs). We provide implementation-level information such as pseudocode workflows, hyperparameter settings, and preprocessing pipelines for popular detection frameworks to improve reproducibility. We also examine the implications of cybersecurity, including identity theft and biometric spoofing, as well as policy-oriented solutions that incorporate federated learning, explainable AI, and ethical protections. By enriching technical insights with interdisciplinary perspectives, this review charts a roadmap for building robust, scalable, and trustworthy deepfake detection systems.
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The most accurate models may not always be the most suitable for live or platform-scale screening without cascading, compression, cloud offloading, or region-of-interest processing.
“the most accurate models may not always be the most suitable for live or platform-scale screening without cascading, compression, cloud offloading, or region-of-interest processing.”
From A Review of Tools and Technologies to Combat Deepfakes (2026) · cites this paper as [49]
Under estimated standard-GPU conditions at 224 × 224 resolution, CNN-based detectors achieve about 88-90% accuracy at roughly 28-30 fps, whereas transformer-based, multimodal, and hybrid detectors reach approximately 92-95% accuracy but often operate at only 18-22 fps.
“Under estimated standard-GPU conditions at 224 × 224 resolution, CNN-based detectors achieve about 88-90% accuracy at roughly 28-30 fps, whereas transformer-based, multimodal, and hybrid detectors reach approximately 92-95% accuracy but often operate at only 18-22 fps [49].”
From A Review of Tools and Technologies to Combat Deepfakes (2026) · cites this paper as [49]
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PathosisGAN: Sick Face Image Synthesis with Generative Adversarial Network
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An end-to-end TTS model with pronunciation predictor
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Detection of Political Manipulation through Unsupervised Learning
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Pose-Guided And Style-Transferred Face Reenactment
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KurdFace Morph Dataset Creation Using OpenCV
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Self-citation
- Authors
- Singh, S., & Dhumane, A.
- Affiliations
- Symbiosis International University (IN)
- Corresponding author
- Singh, Sonam
- Venue
- MethodsX
- Publisher
- Elsevier BV
- Published
- 2025-12
- Volume
- 15
- Pages
- 103632
- ISSN
- 2215-0161 (ISSN-L)
- PMC
- PMC12508882
- OpenAlex
- W4414441547
- Cited by
- 24
- References
- 103
- Citations by year
- 2026: 19 · 2025: 5
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- MethodsX (publishedVersion, cc-by)
- SSRN Electronic Journal (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, deepfake generation, multimedia forensics, generative adversarial networks, face synthesis, biometric spoofing, identity theft, cross-dataset evaluation, federated learning, explainable AI, cybersecurity, misinformation, AI ethics policy
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