Present and Future Generalization of Synthetic Image Detectors

TL;DR

This paper systematically analyzes the generalization of synthetic image detectors, identifying their limitations and providing guidelines to improve robustness and reliability in real-world applications.

Contribution

It offers a comprehensive evaluation of current detectors across diverse conditions and proposes practical strategies to enhance their generalization capabilities.

Findings

Current detectors excel in specific scenarios but lack universal effectiveness.

Significant flaws exist in existing detectors, affecting real-world deployment.

Proposed workarounds improve detector robustness and reliability.

Abstract

The continued release of increasingly realistic image generation models creates a demand for synthetic image detectors. To build effective detectors we must first understand how factors like data source diversity, training methodologies and image alterations affect their generalization capabilities. This work conducts a systematic analysis and uses its insights to develop practical guidelines for training robust synthetic image detectors. Model generalization capabilities are evaluated across different setups (e.g. scale, sources, transformations) including real-world deployment conditions. Through an extensive benchmarking of state-of-the-art detectors across diverse and recent datasets, we show that while current approaches excel in specific scenarios, no single detector achieves universal effectiveness. Critical flaws are identified in detectors, and workarounds are proposed to enable the deployment of real-world detector applications enhancing accuracy, reliability and robustness beyond the limitations of current systems.