针对先进生成模型伪造图像，提出频域与空间域融合的无监督检测方法。
Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Models
- 利用傅里叶谱幅度提取特征，克服谱图中心对称带来的重建难题。
- 在最新神经渲染生成图像上达到92.3%准确率，泛化能力优于现有方法。
- 构建首个基于3D神经渲染的伪造图像数据集，推动检测研究发展。
神经网络驱动的视觉生成技术，尤其是神经辐射场和3D高斯泼溅等神经渲染方法，已能生成高保真图像和逼真虚拟形象，催生了对鲁棒检测方法的需求。为此，本文提出一种无监督训练方法，通过提取傅里叶谱幅度的全面特征，有效应对因中心对称性导致的谱图重建挑战。结合频域与空间域信息动态融合，构建出具有优异泛化能力的多模态检测器，可精准识别最新图像合成技术生成的复杂伪造图像。针对缺乏基于3D神经渲染的伪造图像数据库的问题，本文构建了一个涵盖多种神经渲染技术生成图像的综合性数据库，为检测方法的评估与推进提供了坚实基础。
原文摘要 · Abstract (English)
The remarkable progress in neural-network-driven visual data generation, especially with neural rendering techniques like Neural Radiance Fields and 3D Gaussian splatting, offers a powerful alternative to GANs and diffusion models. These methods can produce high-fidelity images and lifelike avatars, highlighting the need for robust detection methods. In response, an unsupervised training technique is proposed that enables the model to extract comprehensive features from the Fourier spectrum magnitude, thereby overcoming the challenges of reconstructing the spectrum due to its centrosymmetric properties. By leveraging the spectral domain and dynamically combining it with spatial domain information, we create a robust multimodal detector that demonstrates superior generalization capabilities in identifying challenging synthetic images generated by the latest image synthesis techniques. To address the absence of a 3D neural rendering-based fake image database, we develop a comprehensive database that includes images generated by diverse neural rendering techniques, providing a robust foundation for evaluating and advancing detection methods.
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