Dual-Tree Complex Wavelet Driven Hierarchical Spatial-Frequency Fusion Learning for Robust Deepfake Detection

This research proposes a method to detect deepfakes (synthetic videos created by AI models) by analyzing both spatial and frequency-domain features (patterns that emerge when you break down images into different frequency components) using a technique called Dual-Tree Complex Wavelet Transform (DTCWT, a mathematical tool that breaks images into directional components). The method combines two modules: one that captures multi-scale forgery traces across different levels of detail, and another that explicitly models directional patterns in six different frequency bands to improve detection accuracy even when deepfakes become more realistic.