Advanced Machine Learning Techniques to Detect Various Types of Deepfakes
Simon S. Woo · 2022
Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Also, it is challenging to detect different types of deepfake images simultaneously. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capability in KD to enable a student network to learn discriminative features from low-quality images effectively. In addition, we also discuss the continual learning and domain adaptation methods to detect various types of deepfakes simultaneously.