Exploration of Reproducible Generated Image Detection

TL;DR

This paper investigates the reproducibility and generalizability issues in AI-generated image detection by reproducing key methods, analyzing root causes, and providing insights to improve research transparency and robustness.

Contribution

It systematically reproduces existing detection methods, identifies key reproducibility challenges, and offers recommendations for better experimental disclosure and generalization testing.

Findings

Reproducibility is feasible with detailed procedures.

Detection performance drops with preprocessing disruptions.

Cross-generator testing reveals limited generalizability.

Abstract

While the technology for detecting AI-Generated Content (AIGC) images has advanced rapidly, the field still faces two core issues: poor reproducibility and insufficient gen eralizability, which hinder the practical application of such technologies. This study addresses these challenges by re viewing 7 key papers on AIGC detection, constructing a lightweight test dataset, and reproducing a representative detection method. Through this process, we identify the root causes of the reproducibility dilemma in the field: firstly, papers often omit implicit details such as prepro cessing steps and parameter settings; secondly, most detec tion methods overfit to exclusive features of specific gener ators rather than learning universal intrinsic features of AIGC images. Experimental results show that basic perfor mance can be reproduced when strictly following the core procedures described in the original papers. However, de tection performance drops sharply when preprocessing dis rupts key features or when testing across different genera tors. This research provides empirical evidence for improv ing the reproducibility of AIGC detection technologies and offers reference directions for researchers to disclose ex perimental details more comprehensively and verify the generalizability of their proposed methods.