Detecting Deepfake Videos using Attribution-Based Confidence Metric
Steven Lawrence Fernandes, Sunny Raj, Rickard Ewetz, Jodh S. Pannu, Sumit Kumar Jha, Eddy Ortiz, Iustina Vintila, Margaret S. Salter
Abstract
Recent advances in generative adversarial networks have made detecting fake videos a challenging task. In this paper, we propose the application of the state-of-the-art attribution based confidence (ABC) metric for detecting deepfake videos. The ABC metric does not require access to the training data or training the calibration model on the validation data. The ABC metric can be used to draw inferences even when only the trained model is available. Here, we utilize the ABC metric to characterize whether a video is original or fake. The deep learning model is trained only on original videos. The ABC metric uses the trained model to generate confidence values. For, original videos, the confidence values are greater than 0.94.