READFake: Reflection and Environment-Aware DeepFake Detection

This paper presents a novel Reflection and Environment-Aware DeepFake (READFake) detection technique. Using reflections on various body parts (e.g., eyes, nose, cheeks, etc.) and environmental factors, we validate the hypothesis that the existing DeepFake creation methods, including reenactment, replacement, and synthesis, fail to coordinate their counterfeits with the reflective components along with the given environmental mapping. We detect various features from the specular highlight images, including color components, shapes, and textures, to check the coordination with the surrounding environmental factors, such as indoor/outdoor, bright/dark backgrounds, and light strength. We have conducted extensive experiments to evaluate the performance of READFake using various input parameters and advanced Deep Neural Network (DNN) architectures on multiple public DeepFake datasets. The empirical results show that READFake achieves high accuracy (99.00%) in detecting sophisticated DeepFake images.