Individualized Deepfake Detection Exploiting Traces Due to Double
  Neural-Network Operations
Mushfiqur Rahman Runze Liu Chau-Wai Wong Huaiyu Dai
In today's digital landscape, journalists urgently require tools to verify
the authenticity of facial images and videos depicting specific public figures
before incorporating them into news stories. Existing deepfake detectors are
not optimized for this detection task when an image is associated with a
specific and identifiable individual. This study focuses on the deepfake
detection of facial images of individual public figures. We propose to
condition the proposed detector on the identity of the identified individual
given the advantages revealed by our theory-driven simulations. While most
detectors in the literature rely on perceptible or imperceptible artifacts
present in deepfake facial images, we demonstrate that the detection
performance can be improved by exploiting the idempotency property of neural
networks. In our approach, the training process involves double neural-network
operations where we pass an authentic image through a deepfake simulating
network twice. Experimental results show that the proposed method improves the
area under the curve (AUC) from 0.92 to 0.94 and reduces its standard deviation
by 17\%. For evaluating the detection performance of individual public figures,
a facial image dataset with individuals' names is required, a criterion not met
by the current deepfake datasets. To address this, we curated a dataset
comprising 32k images featuring 45 public figures, which we intend to release
to the public after the paper is published.