通过频域特征识别图像真伪，检测准确率提升超12%。
UGAD: Universal Generative AI Detector utilizing Frequency Fingerprints
- 提取图像的频域指纹特征，结合多通道处理增强关键信息
- 在多个数据集上实现12.64%准确率与28.43% AUC提升
- 适用于扩散模型生成图像，适合安全与内容审核场景
在五角大楼伪造图像事件后，辨别真实图像与生成图像的能力愈发关键。本文提出一种新型多模态方法 UGAD，用于检测扩散模型等新一代生成技术产生的假图像。该方法包含三个核心步骤：首先将RGB图像转换为YCbCr通道，并应用积分径向操作以强化显著的径向特征；其次利用空间傅里叶提取操作进行空间位移，结合预训练深度网络实现最优特征提取；最后通过深度神经网络分类阶段，使用全连接层和softmax完成分类。实验表明，相较于现有最先进方法，UGAD在准确率上提升12.64%，AUC提高28.43%，显著增强了真实与生成图像的区分能力。
原文摘要 · Abstract (English)
In the wake of a fabricated explosion image at the Pentagon, an ability to discern real images from fake counterparts has never been more critical. Our study introduces a novel multi-modal approach to detect AI-generated images amidst the proliferation of new generation methods such as Diffusion models. Our method, UGAD, encompasses three key detection steps: First, we transform the RGB images into YCbCr channels and apply an Integral Radial Operation to emphasize salient radial features. Secondly, the Spatial Fourier Extraction operation is used for a spatial shift, utilizing a pre-trained deep learning network for optimal feature extraction. Finally, the deep neural network classification stage processes the data through dense layers using softmax for classification. Our approach significantly enhances the accuracy of differentiating between real and AI-generated images, as evidenced by a 12.64% increase in accuracy and 28.43% increase in AUC compared to existing state-of-the-art methods.
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