FusionNet: A Multi-Modal Hybrid Framework for Deepfake Detection with Adversarial Resistance

Nowhere near perfect, today's methods struggle to tell fake videos apart from real ones. Even experts make mistakes when judging what’s genuine. Tools available right now fall short in delivering clear answers. A better path forward emerges by combining different approaches instead of picking one. FusionNet builds reliability through mixing deep learning with classic techniques. Not relying on just a single method allows stronger results. Looking at footage, the system works through separate layers. First, every single frame gets examined for visible edits. Then, motion between frames comes under review to spot awkward shifts in action. Information from both paths gets filtered before reaching twin classifiers operating together. Because they operate in parallel, one picks up what the other might miss. Outcomes grow stronger when outputs join, beating standalone accuracy. Not only does the system avoid binary responses, but it also reveals its internal logic step by step. In contexts like courtrooms or crime analysis, stating that something was flagged holds little weight without backup. Evidence requires clear paths, not just outcomes. Testing FusionNet using authentic and manipulated video samples showed stronger results, consistently outperforming isolated methods. Under high stakes, clarity paired with precision shifts everything - accuracy alone is never quite enough