ICML 2025

Orthogonal Subspace Decomposition for Generalizable AI-Generated Image
Detection
Zhiyuan Yan * 1 2 Jiangming Wang * 2 Peng Jin 1 Ke-Yue Zhang 2 Chengchun Liu 1 Shen Chen 2 Taiping Yao 2
Shouhong Ding 2 Baoyuan Wu 3 Li Yuan 1
Abstract
Detecting AI-generated images (AIGIs), such as
natural images or face images, has become in-
creasingly important yet challenging. In this pa-
per, we start from a new perspective to exca-
vate the reason behind the failure generalization
in AIGI detection, named the asymmetry phe-
nomenon, where a naively trained detector tends
to favor overfitting to the limited and monotonous
fake patterns, causing the feature space to be-
come highly constrained and low-ranked, which
is proved seriously limiting the expressivity and
generalization. One potential remedy is incorpo-
rating the pre-trained knowledge within the vision
foundation models (higher-ranked) to expand the
feature space, alleviating the model’s overfitting
to fake. To this end, we employ Singular Value
Decomposition (SVD) to decompose the original
feature space into two orthogonal subspaces. By
freezing the principal components and adapting
only the remained components, we preserve the
pre-trained knowledge while learning fake pat-
terns. Compared to existing full-parameters and
LoRA-based tuning methods, we explicitly en-
sure orthogonality, enabling the higher rank of
the whole feature space, effectively minimizing
overfitting and enhancing generalization. We fi-
nally identify a crucial insight: our method im-
plicitly learns a vital prior that fakes are actually
derived from the real, indicating a hierarchical
relationship rather than independence. Modeling
this prior, we believe, is essential for achieving
superior generalization. Our codes are publicly
available at GitHub.
*Equal contribution
1Peking University Shenzhen Gradu-
ate School 2Tencent Youtu Lab 3The Chinese University of
Hong Kong, Shenzhen. Correspondence to: Taiping Yao <taip-
ingyao@tencent.cn>, Li Yuan <yu...