Deepfakebuster: a confidence-calibrated adaptive ensemble framework for robust Deepfake image detection
DeepFakeBuster is presented as a confidence-calibrated adaptive ensemble for deepfake image detection by fusing together heterogeneous deep learning models built around detecting complementary forensic cues e.g., spatial inconsistencies, boundary artifacts, noise residuals, semantic consistency, and frequency-domain features.