WildFake: A Large-scale Challenging Dataset for AI-Generated Images
  Detection
Yan Hong Jianfu Zhang
The extraordinary ability of generative models enabled the generation of
images with such high quality that human beings cannot distinguish Artificial
Intelligence (AI) generated images from real-life photographs. The development
of generation techniques opened up new opportunities but concurrently
introduced potential risks to privacy, authenticity, and security. Therefore,
the task of detecting AI-generated imagery is of paramount importance to
prevent illegal activities. To assess the generalizability and robustness of
AI-generated image detection, we present a large-scale dataset, referred to as
WildFake, comprising state-of-the-art generators, diverse object categories,
and real-world applications. WildFake dataset has the following advantages: 1)
Rich Content with Wild collection: WildFake collects fake images from the
open-source community, enriching its diversity with a broad range of image
classes and image styles. 2) Hierarchical structure: WildFake contains fake
images synthesized by different types of generators from GANs, diffusion
models, to other generative models. These key strengths enhance the
generalization and robustness of detectors trained on WildFake, thereby
demonstrating WildFake's considerable relevance and effectiveness for
AI-generated detectors in real-world scenarios. Moreover, our extensive
evaluation experiments are tailored to yield profound insights into the
capabilities of different levels of generative models, a distinctive advantage
afforded by WildFake's unique hierarchical structure.