The proliferation of deepfake technology poses significant challenges to media authenticity, security, and trust. This project addresses these issues by developing a robust deepfake detection system for videos and images, utilizing advanced deep learning models such as EfficientNet, ResNet50, and their hybrid architectures. By analyzing subtle visual and temporal artifacts, such as inconsistencies in facial movements or unnatural pixel patterns, the system accurately identifies manipulated content. This project is ideal for final-year engineering students aiming to showcase their expertise in artificial intelligence and computer vision, with potential for IEEE publication.
When you purchase this project, you gain access to a complete, end-to-end solution designed to ensure your success. Here's what we offer:
Receive fully functional and tested code tailored to deepfake detection, implemented using Python with TensorFlow or PyTorch for EfficientNet, ResNet50, and hybrid models.
We assist in implementing the project on your system, ensuring smooth integration and providing full support throughout the process.
Get detailed documentation, including reports, PPTs, and datasets for research papers. We ensure you have all materials needed for successful presentation and publication.
Benefit from ongoing mentorship and support. Whether you encounter errors or need improvements, we're here to help every step of the way.
This is one of the best IEEE Artificial Intelligence project ideas for final-year students. The project includes complete frontend (HTML, CSS, JavaScript) and backend (Python) codes, along with detailed explanations to ensure thorough understanding. We also provide content for your report and IEEE paper publication, making it a commercially viable solution for applications in media, security, and beyond.