Core Viewpoint - The article discusses the advancements in AI-generated image detection, particularly focusing on the challenges of distinguishing between real and generated images, emphasizing the complexity beyond simple binary classification [1][5][31]. Group 1: Research Findings - A study conducted by researchers from Peking University and Tencent Youtu Lab reveals that AI-generated image detection is more complex than a straightforward "real-fake" binary classification [1][5]. - The research introduces a new solution based on orthogonal subspace decomposition, which enhances the generalization ability of detection models from "memorization" to "understanding" [1][3][31]. - The study highlights the asymmetry in the binary classification of AI-generated images, where models tend to overfit to fixed fake patterns in the training set, limiting their generalization capabilities [5][7][9]. Group 2: Methodology - The proposed method utilizes Singular Value Decomposition (SVD) to create two orthogonal subspaces: one for retaining pre-trained knowledge and another for learning new AIGI-related knowledge [16][18]. - The approach involves freezing the principal components while fine-tuning the residual components, allowing the model to learn fake detection information while preserving original knowledge [17][18][25]. - The method's effectiveness is validated through attention map visualizations, demonstrating the orthogonality between retained semantic information and learned fake features [25][27]. Group 3: Experimental Results - The proposed method shows improved generalization performance in tasks such as DeepFake face detection and AIGC full-image generation detection, outperforming traditional methods [21][23]. - Quantitative analysis indicates that traditional methods lead to a significant reduction in the effective dimensionality of the feature space, while the new method maintains a high-rank feature space [10][14][22]. Group 4: Insights and Future Directions - The article emphasizes that the relationship between real and fake images is hierarchical rather than independent, suggesting that understanding this relationship is crucial for effective detection [29][30]. - The research proposes that the orthogonal decomposition framework can be applied to other AI tasks, providing a new paradigm for balancing existing knowledge with adaptability in new domains [31].