随着 AIGC 技术的发展,由 AI 所生成的虚假图像的检测问题正成为一个新的研究热点。但是现有检测方法的实验条件与测试数据集不完全相同,无法直接横向对比检测性能。为此,我们针对目前现有主流AIGC图像检测方案进行全面分析与比较。在保证相同训练集与实验条件的情况下,分析测试目前检测算法的检测准确率与泛化性等性能指标,为AIGC生成图像检测领域提供一项基准的实验对比平台(Benchmark),同时整合并开源了多种现有AIGC生成图像的检测算法。
[1]Lago F, Pasquini C, Böhme R, et al. More real than real: A study on human visual perception of synthetic faces [applications corner][J]. IEEE Signal Processing Magazine, 2021, 39(1): 109-116. [2]Wang S Y, Wang O, Zhang R, et al. CNN-generated images are surprisingly easy to spot... for now[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 8695-8704. [3]Frank J, Eisenhofer T, Schönherr L, et al. Leveraging frequency analysis for deep fake image recognition[C]//International conference on machine learning. PMLR, 2020: 3247-3258. [4]Liu Z, Qi X, Torr P H S. Global texture enhancement for fake face detection in the wild[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 8060-8069. [5]Ju Y, Jia S, Ke L, et al. Fusing global and local features for generalized ai-synthesized image detection[C]//2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022: 3465-3469. [6]Liu B, Yang F, Bi X, et al. Detecting generated images by real images[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 95-110. [7]Tan C, Zhao Y, Wei S, et al. Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 12105-12114. [8]Ojha U, Li Y, Lee Y J. Towards universal fake image detectors that generalize across generative models[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023: 24480-24489. [9]Wang Z, Bao J, Zhou W, et al. DIRE for Diffusion-Generated Image Detection[J]. arXiv preprint arXiv:2303.09295, 2023. [10]Zhong N, Xu Y, Qian Z, et al. Rich and Poor Texture Contrast: A Simple yet Effective Approach for AI-generated Image Detection[J]. arXiv preprint arXiv:2311.12397, 2023. [11]Zhu M, Chen H, Yan Q, et al. GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image[J]. arXiv preprint arXiv:2306.08571, 2023.