Redefining Generalization in Visual Domains: A Two-Axis Framework for Fake Image Detection with FusionDetect

• URL: http://arxiv.org/abs/2510.05740v1
• Date: Tue, 07 Oct 2025 10:01:32 GMT
• Title: Redefining Generalization in Visual Domains: A Two-Axis Framework for Fake Image Detection with FusionDetect
• Abstract summary: We present the OmniGen Benchmark to evaluate detector performance under realistic conditions.<n>We also introduce a new method, FusionDetect, aimed at addressing both vectors of generalization.<n>Our experiments demonstrate that FusionDetect delivers not only a new state-of-the-art, but also a 4.48% increase in accuracy on OmniGen.
• Score: 19.97092710696699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of generative models has made it increasingly crucial to develop detectors that can reliably detect synthetic images. Although most of the work has now focused on cross-generator generalization, we argue that this viewpoint is too limited. Detecting synthetic images involves another equally important challenge: generalization across visual domains. To bridge this gap,we present the OmniGen Benchmark. This comprehensive evaluation dataset incorporates 12 state-of-the-art generators, providing a more realistic way of evaluating detector performance under realistic conditions. In addition, we introduce a new method, FusionDetect, aimed at addressing both vectors of generalization. FusionDetect draws on the benefits of two frozen foundation models: CLIP & Dinov2. By deriving features from both complementary models,we develop a cohesive feature space that naturally adapts to changes in both thecontent and design of the generator. Our extensive experiments demonstrate that FusionDetect delivers not only a new state-of-the-art, which is 3.87% more accurate than its closest competitor and 6.13% more precise on average on established benchmarks, but also achieves a 4.48% increase in accuracy on OmniGen,along with exceptional robustness to common image perturbations. We introduce not only a top-performing detector, but also a new benchmark and framework for furthering universal AI image detection. The code and dataset are available at http://github.com/amir-aman/FusionDetect

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