A New Gen-AI Detector

Prior fake image detection methods [1, 2] learn to detect fake images by simply training a classifier on the Original images. We wish to leverage a pre-trained diffusion model (Stable Diffusion 1.5) to train a better detection model.

To do this, we obtain additional input features by using DDIM Inversion and then use the Original, Inverted, Reconstructed together as the input to our model.

Despite only using features extracted from a weak generator (SD v1.5), our method generalizes remarkably well and can detect fake images from modern propriatary (DALL-E 3, Midjourney, Imagen, etc.) and open-source (Kandinsky, PixArt-α, etc.) generators that were never seen during training.

A New Evaluation Benchmark

Existing evaluation methods for GenAI detectors only use LAION images as the "real" samples in the test set, despite generated "fake" images typically having very different "styles" than the "real" LAION images (see above). Unfortunately, this often results in a style mismatch between the "real" and "fake" test images, calling into question whether or not the model is actually detecting "realness" or merely some kind of style bias.

To help alleviate these concerns, we introduce a new set of benchmarks intended to show how well a model would perform in-the-wild. We do this by searching the (Pre-GenAI) Internet for images that most closely match the style and content of the "fake" images obtained from the generative model in question.

When collected this way, the distribution of "real" test images is much closer than LAION to that of the "fake" images from the generator.

Image Browsing

Please use the following links to view previews of our new test sets. Fake images are on the left, and the corresponding Real images found via reverse image search are on the right.