  Computer mediated conversations (e.g., videoconferencing) is now the new
mainstream media. How would credibility be impacted if one could change their
race on the fly in these environments? We propose an approach using Deepfakes
and a supporting GAN architecture to isolate visual features and alter racial
perception. We then crowd-sourced over 800 survey responses to measure how
credibility was influenced by changing the perceived race. We evaluate the
effect of showing a still image of a Black person versus a still image of a
White person using the same audio clip for each survey. We also test the effect
of showing either an original video or an altered video where the appearance of
the person in the original video is modified to appear more White. We measure
credibility as the percent of participant responses who believed the speaker
was telling the truth. We found that changing the race of a person in a static
image has negligible impact on credibility. However, the same manipulation of
race on a video increases credibility significantly (61\% to 73\% with p $<$
0.05). Furthermore, a VADER sentiment analysis over the free response survey
questions reveals that more positive sentiment is used to justify the
credibility of a White individual in a video.
