Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security. Detecting fake images is of paramount importance to prevent illegal activities, and previous research has shown that generative models leave unique patterns in their synthetic images that can be exploited to detect them. However, the fundamental problem of generalization remains, as even state-of-the-art detectors encounter difficulty when facing generators never seen during training. To assess the generalizability and robustness of synthetic image detectors in the face of real-world impairments, this paper presents a large-scale dataset named ArtiFact, comprising diverse generators, object categories, and real-world challenges. Moreover, the proposed multi-class classification scheme, combined with a filter stride reduction strategy addresses social platform impairments and effectively detects synthetic images from both seen and unseen generators. The proposed solution significantly outperforms other top teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% on Test 3 in the IEEE VIP Cup challenge at ICIP 2022, as measured by the accuracy metric.

翻译：合成图像生成技术带来了新机遇，但也对隐私、真实性和安全性构成了威胁。检测虚假图像对于防止非法活动至关重要，已有研究表明生成模型会在其合成图像中留下可被利用的独特模式。然而，泛化这一根本问题依然存在——即使最先进的检测器在面临训练中从未见过的生成器时也困难重重。为评估合成图像检测器在真实世界干扰下的泛化能力和鲁棒性，本文提出了名为ArtiFact的大规模数据集，其涵盖多样化的生成器、物体类别及真实世界挑战。此外，所提出的多类分类方案结合滤波器步长缩减策略，可应对社交平台造成的干扰，并有效检测来自已见与未见生成器的合成图像。在ICIP 2022的IEEE VIP Cup挑战赛中，该方案在测试集1、2、3上分别以8.34%、1.26%和15.08%的准确率优势显著优于其他顶尖团队。