Hybrid deep feature integration model for robust deepfake detection using transfer-learned neural networks

Experiments supervised on Datasets of Celeb- DF(v2) and Kaggle exhibit that the proposed hybrid approach enhances macro- F1, calibration error, and temporal robustness compared to baseline models. The proposed model obtains a competitive outcome under constrained computational resources, making it appropriate for forensic applications, real-world media authentication systems, low-power deployments, and scalable deepfake screening pipelines.

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Publication Details

Journal
Frontiers in Artificial Intelligence
Published
2026-02-25
DOI
https://doi.org/10.3389/frai.2026.1737761
Primary Topic
Digital Media Forensic Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Hybrid deep feature integration model for robust deepfake detection using transfer-learned neural networks

Sachi Nandan Mohanty, Kailash Chandra Rout, Sirisha Potluri, Mohammad Israr et al.
Frontiers in Artificial Intelligence
Digital Media Forensic Detection
article

Hybrid deep feature integration model for robust deepfake detection using transfer-learned neural networks

Sachi Nandan Mohanty, Kailash Chandra Rout, Sirisha Potluri, Mohammad Israr, V. Mnssvkr Gupta, Srikar Prabhas Kandagatla
article en

Abstract

Experiments supervised on Datasets of Celeb- DF(v2) and Kaggle exhibit that the proposed hybrid approach enhances macro- F1, calibration error, and temporal robustness compared to baseline models. The proposed model obtains a competitive outcome under constrained computational resources, making it appropriate for forensic applications, real-world media authentication systems, low-power deployments, and scalable deepfake screening pipelines.

Frontiers in Artificial IntelligenceVol. 9
Amherst College (US), University of Massachusetts Amherst (US), Capital Group (United States) (US), Maryam Abacha American University of Niger (NE), Capital Health (US), SRKR Engineering College, Koneru Lakshmaiah Education Foundation (IN)
Openalex Percentile: Top 46%
Digital Media Forensic Detection
DOIOpen Access PDFOpenAlex
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