ImagiNet: A Multi-Content Benchmark for Synthetic Image Detection

Synthetic images are becoming dangerously realistic: Recent AI generative models now produce images that are nearly indistinguishable from authentic photos and artwork, creating urgent need for detection tools to prevent misuse.

Existing detection datasets are too narrow: Current synthetic image detection datasets either contain low-quality generated images or focus on only one type of content (like faces), which means detectors trained on them fail when encountering different types of synthetic images in the real world.

Diversity and balance are essential for reliable detection: ImagiNet's key finding is that detectors perform much better when trained on a balanced dataset spanning multiple content types—photos, paintings, faces, and miscellaneous images—rather than specialized datasets focused on single categories.

Current detection technology shows promise but needs testing: The baseline detector achieved up to 99% accuracy in identifying synthetic images and could identify which AI generator created them, even when images were compressed or resized, suggesting detection is technically feasible at scale.

New benchmark enables better real-world evaluation: By providing 200,000 examples across diverse content types from both open-source and proprietary generators, ImagiNet allows researchers to develop and test detectors that will actually work against the variety of synthetic images circulating online.