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Synthetic image generation has opened up new opportunities but has alsocreated threats in regard to privacy, authenticity, and security. Detectingfake images is of paramount importance to prevent illegal activities, andprevious research has shown that generative models leave unique patterns intheir synthetic images that can be exploited to detect them. However, thefundamental problem of generalization remains, as even state-of-the-artdetectors encounter difficulty when facing generators never seen duringtraining. To assess the generalizability and robustness of synthetic imagedetectors in the face of real-world impairments, this paper presents alarge-scale dataset named ArtiFact, comprising diverse generators, objectcategories, and real-world challenges. Moreover, the proposed multi-classclassification scheme, combined with a filter stride reduction strategyaddresses social platform impairments and effectively detects synthetic imagesfrom both seen and unseen generators. The proposed solution significantlyoutperforms other top teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% onTest 3 in the IEEE VIP Cup challenge at ICIP 2022, as measured by the accuracymetric.

ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection