Visible to the public Biblio

Filters: Author is Kumar Jha, Sumit  [Clear All Filters]
Fernandes, Steven, Raj, Sunny, Ewetz, Rickard, Pannu, Jodh Singh, Kumar Jha, Sumit, Ortiz, Eddy, Vintila, Iustina, Salter, Margaret.  2020.  Detecting Deepfake Videos using Attribution-Based Confidence Metric. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). :1250–1259.
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.