Kdd 2025 Improving Synthetic Image Detection Towards Generalization

Registration – KDD 2025

Registration – KDD 2025 Comparative experiments are conducted on an open world dataset, comprising synthetic images generated by 26 distinct generative models. our pipeline achieves a new state of the art performance, with remarkable improvements of 4.5% in accuracy and 2.9% in average precision against existing methods. Here, we evaluate the generalization performance of our safe on this front, using two subsets collected by gpt imgeval: geneval (555 fake images) and reasoningedit (190 fake images).

Call For Hands-On Tutorials – KDD 2025

Call For Hands-On Tutorials – KDD 2025 About press copyright contact us creators advertise developers terms privacy policy & safety how works test new features nfl sunday ticket © 2025 google llc. 要实现通用的 ai 图像检测，核心问题是如何泛化到未知的生成模型上去，现在主流的生成模型包括生成对抗网络 gans 和扩散模型 dms。 研究团队从生成模型架构的共性出发，期望从 ai 图像和真实图像的成像机制的差异中找到突破口。 在 gans 中，先通过全连接层把低分辨率的潜在特征变成高分辨率，然后用上采样和卷积操作合成图像。 dms 呢，先把有噪图像通过池化和卷积操作降维，再通过同样的操作升维预测噪声。 这两种模型在合成图像时，都大量使用上采样和卷积，而这两个操作在数值计算上相当于对像素值加权平均，会让合成图像相邻像素的局部相关性变强，留下独特的 “伪影特征”，这就是 ai 图像检测的关键线索。. Improving synthetic image detection towards generalization: an image transformation perspective. in proceedings of the 31st acm sigkdd conference on knowledge discovery and data mining v.1 (kdd ’25), august 3–7, 2025, toronto, on, canada. Comparative experiments are conducted on an open world dataset, comprising synthetic images generated by 26 distinct generative models. our pipeline achieves a new state of the art performance, with remarkable improvements of 4.5% in accuracy and 2.9% in average precision against existing methods.

Figure 1 From Improving Synthetic Image Detection Towards Generalization: An Image ...

Figure 1 From Improving Synthetic Image Detection Towards Generalization: An Image ... Improving synthetic image detection towards generalization: an image transformation perspective. in proceedings of the 31st acm sigkdd conference on knowledge discovery and data mining v.1 (kdd ’25), august 3–7, 2025, toronto, on, canada. Comparative experiments are conducted on an open world dataset, comprising synthetic images generated by 26 distinct generative models. our pipeline achieves a new state of the art performance, with remarkable improvements of 4.5% in accuracy and 2.9% in average precision against existing methods. Here, we evaluate the generalization performance of our safe on this front, using two subsets collected by gpt imgeval: geneval (555 fake images) and reasoningedit (190 fake images). In this paper, we re examine the sid problem and identify two prevalent biases in current training paradigms, i.e., weakened artifact features and overfitted artifact features. This work conducts a systematic analysis and uses its insights to develop practical guidelines for training robust synthetic image detectors. model generalization capabilities are evaluated across different setups (e.g. scale, sources, transformations) including real world deployment conditions. About press copyright contact us creators advertise developers terms privacy policy & safety how works test new features nfl sunday ticket © 2025 google llc.

KDD 2025 - Improving Synthetic Image Detection Towards Generalization