  With growing abilities of generative models, artificial content detection
becomes an increasingly important and difficult task. However, all popular
approaches to this problem suffer from poor generalization across domains and
generative models. In this work, we focus on the robustness of AI-generated
image (AIGI) detectors. We analyze existing state-of-the-art AIGI detection
methods based on frozen CLIP embeddings and show how to interpret them,
shedding light on how images produced by various AI generators differ from real
ones. Next we propose two ways to improve robustness: based on removing harmful
components of the embedding vector and based on selecting the best performing
attention heads in the image encoder model. Our methods increase the mean
out-of-distribution (OOD) classification score by up to 6% for cross-model
transfer. We also propose a new dataset for AIGI detection and use it in our
evaluation; we believe this dataset will help boost further research. The
dataset and code are provided as a supplement.
