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 outperforms other teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% on Test 3 in the IEEE VIP CUP at ICIP 2022.

翻译：合成图像生成技术在带来新机遇的同时，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，即便是最先进的检测器在面对训练阶段未见过的生成器时也难以应对。为评估合成图像检测器面对真实世界干扰时的泛化能力与鲁棒性，本文提出名为ArtiFact的大规模数据集，包含多种生成器、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，能够应对社交平台图像退化问题，并有效检测来自已知与未知生成器的合成图像。该方案在2022年ICIP会议的IEEE VIP CUP竞赛中，分别以8.34%、1.26%和15.08%的性能优势在测试集一、二、三中超越其他参赛队伍。