Deepfake Detection and Authentication Using Hybrid Artificial Intelligence Models: A Case Study
Bibliographic record
Abstract
The progress of artificial intelligence (AI) has enabled the creation of very realistic synthetic media, also known as deepfakes, which poses a serious threat to information integrity and social confidence. The article examined the process of detecting and authenticating deep fakes using hybrid AI models. The researchers employed the case study methodology, based on the Celeb-DF V2 dataset, one of the most challenging datasets for generating high-quality manipulated videos. The suggested system combined convolutional neural networks (CNNs) to extract spatial features, recurrent neural networks (LSTMs/GRUs) to model temporal consistency, and transformer systems to analyse fine-grained context. The researchers bundled these parts together to enhance robustness and generalisation in an ensemble mechanism. They also introduced provenance tracking and semi-fragile watermarking to supplement detection, enabling proactive authentication and watermark verification of media through blockchain-based provenance tracking. The experimental findings showed that the hybrid models were more accurate, achieved higher F1 Scores, and were more robust to adversarial manipulations than the single-model baselines. The hybrid with a transformer achieved the best accuracy (0.95 AUC) and the lowest false-positive rate (6%), but at the expense of slower processing speeds. Authentication tools also helped strengthen trust by verifying the originality of content and flagging potential manipulation before it was classified. The results have revealed that hybrid AI models, when implemented with authentication strategies, represent a more effective and legitimate approach to addressing the threats of misinformation, fraud, and loss of trust among the population in the face of deepfakes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".