Exploring AI/ML Solutions to Deepfake Detection and Prevention | IJCT Volume 12 – Issue 6 | IJCT-V12I6P60

Even with some improvements in spotting and preventing deepfakes, the methods we have now still aren’t great at working under different situations, being fast, or holding up in the real world. Here are some things that could be done to improve them: •Hybrid AI–Blockchain Framework Using deep learning models (like CNNs, Capsule Networks, or Vision Transformers) along with blockchain to verify origin of content. This way, we can prove whether the content is authentic and spot fakes, building trust and tracking things better. •Multi-Modal Detection Systems Right now, most models just look at the pictures or videos. Adding sound, text, and even things like heartbeat or blinking patterns could help the models be right more often and spot different kinds of deepfakes. •Lightweight and Efficient Models If we can make the models use fewer resources by cutting out unnecessary parts or simplifying them, they can work faster and in real-time. That means we could use them on phones or other devices. •Adversarial and Continual Learning Training the models all the time and using methods to make them stronger against tricks will help them keep up with how deepfakes keep getting better. •Standardized Benchmarks and Open Datasets Having bigger, better sets of examples to test the models on—with real-world fakes, different quality levels, and samples from different sources—would help us see which models are really the best. •Explainable AI (XAI) Integration Adding tools that show why a model flagged something as fake would help people trust the system more. It would let experts see how the model is making its decisions. •Policy and Cross-Platform Collaboration Besides the tech stuff, social media companies, governments, and researchers should work together to set up rules for checking content and dealing with new deepfakes quickly.