The creation of sophisticated fake videos has been largely relegated to Hollywood studios or state actors. Recent advances in deep learning, however, have made it significantly easier to create sophisticated and compelling fake videos. With relatively modest amounts of data and computing power, the average person can, for example, create a video of a world leader confessing to illegal activity leading to a constitutional crisis, a military leader saying something racially insensitive leading to civil unrest in an area of military activity, or a corporate titan claiming that their profits are weak leading to global stock manipulation. These so called deep fakes pose a significant threat to our democracy, national security, and society. To contend with this growing threat, we describe a forensic technique that models facial expressions and movements that typify an individual’s speaking pattern. Although not visually apparent, these correlations are often violated by the nature of how deep-fake videos are created and can, therefore, be used for authentication.
We hypothesize that as an individual speaks, they have distinct (but probably not unique) facial expressions and movements. Given a single video as input, we begin by tracking facial and head movements and then extracting the presence and strength of specific action units . We then build a novelty detection model (one-class support vector machine (SVM) ) that distinguishes an individual from other individuals as well as comedic impersonators and deep-fake impersonators.
Shown in Figure 3 is a 2-D t-SNE  visualization of the 190-dimensional features for Hillary Clinton, Barack Obama, Bernie Sanders, Donald Trump, Elizabeth Warren, random people , and lip-sync deep fake of Barack Obama. Notice that in this low-dimensional representation, the POIs are well separated from each other. This shows that the proposed correlations of action units and head movements can be used to discriminate between individuals. We also note that this visualization supports the decision to use a one-class support vector machine (SVM). In particular, were we to train a two-class SVM to distinguish Barack Obama (light gray) from random people (pink), then this classifier would almost entirely misclassify deep fakes (dark gray with black border).
The performance of each POI-specific model is tested using the POI-specific comedic impersonators and deep fakes, Section 2.2. We report the testing accuracy as the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the true positive rate (TPR) of correctly recognizing an original at fixed false positive rates (FPR) of 1%, 5%, and 10%. These accuracies are reported for both the 10-second clips and the full-video segments. A video segment is classified based on the median SVM score of all overlapping 10-second clips. We first present a detailed analysis of the original and fake Barack Obama videos, followed by an analysis of the other POIs.