WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection

TL;DR

WildFake is a large, diverse dataset designed to improve the detection of AI-generated images, addressing challenges of generalization and robustness in real-world scenarios.

Contribution

This paper introduces WildFake, a comprehensive dataset with diverse, hierarchical AI-generated images to enhance detection methods' robustness and generalizability.

Findings

WildFake improves detection robustness across various generative models

Detectors trained on WildFake outperform those trained on smaller datasets

Hierarchical structure aids in understanding different generative techniques

Abstract

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.