  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.
