Real, fake and synthetic faces -- does the coin have three sides?
Shahzeb Naeem Ramzi Al-Sharawi Muhammad Riyyan Khan Usman Tariq Abhinav Dhall Hasan Al-Nashash
With the ever-growing power of generative artificial intelligence, deepfake
and artificially generated (synthetic) media have continued to spread online,
which creates various ethical and moral concerns regarding their usage. To
tackle this, we thus present a novel exploration of the trends and patterns
observed in real, deepfake and synthetic facial images. The proposed analysis
is done in two parts: firstly, we incorporate eight deep learning models and
analyze their performances in distinguishing between the three classes of
images. Next, we look to further delve into the similarities and differences
between these three sets of images by investigating their image properties both
in the context of the entire image as well as in the context of specific
regions within the image. ANOVA test was also performed and provided further
clarity amongst the patterns associated between the images of the three
classes. From our findings, we observe that the investigated deeplearning
models found it easier to detect synthetic facial images, with the ViT Patch-16
model performing best on this task with a class-averaged sensitivity,
specificity, precision, and accuracy of 97.37%, 98.69%, 97.48%, and 98.25%,
respectively. This observation was supported by further analysis of various
image properties. We saw noticeable differences across the three category of
images. This analysis can help us build better algorithms for facial image
generation, and also shows that synthetic, deepfake and real face images are
indeed three different classes.