Deepfakebuster: a confidence-calibrated adaptive ensemble framework for robust Deepfake image detection

Abstract

Advances in the realism of synthetic media created with generative adversarial networks (GANs), diffusion models, and face manipulation tools has created an increased demand for well-established deepfake detection systems that can detect many different types of manipulation artifacts. However, most single model deepfake detectors are not very robust because they rely on specific forensic cues and do not adapt well to shifts in how synthesis occurs. We present DeepFakeBuster 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. In contrast to traditional ensemble approaches that use static averaging of detector outputs, our proposed framework utilizes reliability aware adaptive fusion where the contribution of each detector to the fused output is adjusted dynamically through the use of reliability priors derived from validation and input-specific confidence estimates. Our experimental evaluation on a dataset comprised of 192,000 authentic and manipulated images shows that our ensemble significantly outperforms both individual constituent detectors as well as static fusion baselines, with an overall accuracy of 97.8% for the evaluated conditions. Additionally, an interpretable forensic analysis module provides visual and quantitative indicators associated with manipulation-sensitive regions. The findings suggest that confidence-aware heterogeneous ensemble learning represents a promising direction for robust deepfake detection.