Qasim Abbas, Nida Mohsin, Mohammad Shah, Muhammad Awais Ali, Ayesha Yousaf and Farhad Ali Farhat 2026. Fauxshield: An Explainable Deepfake Detection and Awareness Platform Using Hybrid Deep Learning. 13th International Conference on Future Internet of Things and Cloud (FiCloud). Granada, Spain 20 - 22 Jul 2026 IEEE . https://doi.org/10.1109/ficloud70576.2026.00046
| Title | Fauxshield: An Explainable Deepfake Detection and Awareness Platform Using Hybrid Deep Learning |
|---|---|
| Authors | Qasim Abbas, Nida Mohsin, Mohammad Shah, Muhammad Awais Ali, Ayesha Yousaf and Farhad Ali Farhat |
| Type | Conference paper |
| Abstract | Deepfake technology is a risk for misinformation, privacy and security. The platform we propose uses Deep Learning and Explainable AI to find deepfakes in images and videos. It has an user-friendly design. The system tries to solve the AI “black-box” problem by using LIME to explain how it works. It also includes resources to help people understand more. Our platform is built with PHP Laravel and TensorFlow. It has an API that allows other applications to use it. The goal is to make deepfake analysis easy to use for everyone: journalists, researchers organizations and regular people. The Xception model for images is expected to be 92% accurate. The CNN+RNN model for videos is expected to be 85% accurate. The overall accuracy is expected to be 88%. Our platform, FauxShield is different, from detection systems. It helps users understand deepfakes and their impact. We provide explanations of how we make our decisions. We offer support in many languages. This way we want to help people identify deepfakes and know what they mean. |
| Year | 2026 |
| Conference | 13th International Conference on Future Internet of Things and Cloud (FiCloud) |
| Publisher | IEEE |
| Accepted author manuscript | License CC BY 4.0 File Access Level Open (open metadata and files) |
| Publication dates | |
| Published | 26 Aug 2026 |
| Journal | 2026 13th International Conference on Future Internet of Things and Cloud (FiCloud) |
| ISBN | 9798319547392 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ficloud70576.2026.00046 |
| Web address (URL) | http://dx.doi.org/10.1109/ficloud70576.2026.00046 |