Robustness and Generalizability of Deepfake Detection: A Study with
  Diffusion Models
Haixu Song Shiyu Huang Yinpeng Dong Wei-Wei Tu
The rise of deepfake images, especially of well-known personalities, poses a
serious threat to the dissemination of authentic information. To tackle this,
we present a thorough investigation into how deepfakes are produced and how
they can be identified. The cornerstone of our research is a rich collection of
artificial celebrity faces, titled DeepFakeFace (DFF). We crafted the DFF
dataset using advanced diffusion models and have shared it with the community
through online platforms. This data serves as a robust foundation to train and
test algorithms designed to spot deepfakes. We carried out a thorough review of
the DFF dataset and suggest two evaluation methods to gauge the strength and
adaptability of deepfake recognition tools. The first method tests whether an
algorithm trained on one type of fake images can recognize those produced by
other methods. The second evaluates the algorithm's performance with imperfect
images, like those that are blurry, of low quality, or compressed. Given varied
results across deepfake methods and image changes, our findings stress the need
for better deepfake detectors. Our DFF dataset and tests aim to boost the
development of more effective tools against deepfakes.