DeepFake Detection Using Multi-Modal Deep Learning with Explainable AI
Abstract
This research proposes a multi-modal deepfake detection system that analyzes text, images, and videos using deep learning techniques. The proposed model integrates DistilBERT for text analysis and convolutional neural networks for visual media detection. Explainable AI techniques such as SHAP are used to improve model interpretability and transparency. Experimental results demonstrate improved accuracy and robustness in detecting manipulated media compared to existing methods.
Fields of Study & Subjects
Digital Media Forensic Detection
Misinformation and Its Impacts
Advanced Steganography and Watermarking Techniques
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 5 March 2026
- Language
- EN
- Document type
- Preprint