Data-Independent Operator: A Training-Free Artifact Representation
  Extractor for Generalizable Deepfake Detection
Chuangchuang Tan Ping Liu RenShuai Tao Huan Liu Yao Zhao Baoyuan Wu Yunchao Wei
Recently, the proliferation of increasingly realistic synthetic images
generated by various generative adversarial networks has increased the risk of
misuse. Consequently, there is a pressing need to develop a generalizable
detector for accurately recognizing fake images. The conventional methods rely
on generating diverse training sources or large pretrained models. In this
work, we show that, on the contrary, the small and training-free filter is
sufficient to capture more general artifact representations. Due to its unbias
towards both the training and test sources, we define it as Data-Independent
Operator (DIO) to achieve appealing improvements on unseen sources. In our
framework, handcrafted filters and the randomly-initialized convolutional layer
can be used as the training-free artifact representations extractor with
excellent results. With the data-independent operator of a popular classifier,
such as Resnet50, one could already reach a new state-of-the-art without bells
and whistles. We evaluate the effectiveness of the DIO on 33 generation models,
even DALLE and Midjourney. Our detector achieves a remarkable improvement of
$13.3\%$, establishing a new state-of-the-art performance. The DIO and its
extension can serve as strong baselines for future methods. The code is
available at
\url{https://github.com/chuangchuangtan/Data-Independent-Operator}.