Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection

• URL: http://arxiv.org/abs/2411.15633v2
• Date: Fri, 31 Jan 2025 17:31:54 GMT
• Title: Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
• Authors: Zhiyuan Yan, Jiangming Wang, Peng Jin, Ke-Yue Zhang, Chengchun Liu, Shen Chen, Taiping Yao, Shouhong Ding, Baoyuan Wu, Li Yuan,
• Abstract summary: A naively trained detector tends to favor overfitting to the limited and monotonous fake patterns, causing the feature space to become highly constrained and low-ranked.<n>One potential remedy is incorporating the pre-trained knowledge within the vision foundation models to expand the feature space.<n>By freezing the principal components and adapting only the remained components, we preserve the pre-trained knowledge while learning forgery-related patterns.
• Score: 58.87142367781417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AI-generated images (AIGIs), such as natural or face images, have become increasingly realistic and indistinguishable, making their detection a critical and pressing challenge. In this paper, we start from a new perspective to excavate the reason behind the failure generalization in AIGI detection, named the \textit{asymmetry phenomenon}, where a naively trained detector tends to favor overfitting to the limited and monotonous fake patterns, causing the feature space to become highly constrained and low-ranked, which is proved seriously limiting the expressivity and generalization. One potential remedy is incorporating 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 forgery-related patterns. Compared to existing full-parameters and LoRA-based tuning methods, we explicitly ensure orthogonality enabling the higher rank of the whole feature space, effectively minimizing overfitting and enhancing generalization. Extensive experiments with our deep analysis on both deepfake and synthetic image detection benchmarks demonstrate superior generalization performance in detection.

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