# A Hybrid Deep Learning and Forensic Approach for Robust Deepfake Detection

arXiv: https://arxiv.org/abs/2510.27392 · HTML: https://mlsanity.com/paper/2510.27392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics](https://mlsanity.com/paper/1909.12962/) (2019)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions](https://mlsanity.com/paper/2003.01826/) (2020)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2108.12947/) (2021)
- [On Improving Cross-dataset Generalization of Deepfake Detectors](https://mlsanity.com/paper/2204.04285/) (2022)
- [Improved Forecasting Using a PSO-RDV Framework to Enhance Artificial Neural Network](https://mlsanity.com/paper/2402.18576/) (2024)
- [Cross-Dataset-Robust Method for Blind Real-World Image Quality Assessment](https://mlsanity.com/paper/2309.14868/) (2023)
- [Cross-Dataset Generalization in Deep Learning](https://mlsanity.com/paper/2410.11207/) (2024)

## Built on by (most cited first, 1 of 1)

- [Benchmarking Federated Learning in Edge Computing Environments: A Systematic Review and Performance Evaluation](https://mlsanity.com/paper/2603.08735/) (2026)

## Abstract (arXiv)

The rapid evolution of generative adversarial networks (GANs) and diffusion models has made synthetic media increasingly realistic, raising societal concerns around misinformation, identity fraud, and digital trust. Existing deepfake detection methods either rely on deep learning, which suffers from poor generalization and vulnerability to distortions, or forensic analysis, which is interpretable but limited against new manipulation techniques. This study proposes a hybrid framework that fuses forensic features, including noise residuals, JPEG compression traces, and frequency-domain descriptors, with deep learning representations from convolutional neural networks (CNNs) and vision transformers (ViTs). Evaluated on benchmark datasets (FaceForensics++, Celeb-DF v2, DFDC), the proposed model consistently outperformed single-method baselines and demonstrated superior performance compared to existing state-of-the-art hybrid approaches, achieving F1-scores of 0.96, 0.82, and 0.77, respectively. Robustness tests demonstrated stable performance under compression (F1 = 0.87 at QF = 50), adversarial perturbations (AUC = 0.84), and unseen manipulations (F1 = 0.79). Importantly, explainability analysis showed that Grad-CAM and forensic heatmaps overlapped with ground-truth manipulated regions in 82 percent of cases, enhancing transparency and user trust. These findings confirm that hybrid approaches provide a balanced solution, combining the adaptability of deep models with the interpretability of forensic cues, to develop resilient and trustworthy deepfake detection systems.