  The advancement in numerous generative models has a two-fold effect: a simple
and easy generation of realistic synthesized images, but also an increased risk
of malicious abuse of those images. Thus, it is important to develop a
generalized detector for synthesized images of any GAN model or object
category, including those unseen during the training phase. However, the
conventional methods heavily depend on the training settings, which cause a
dramatic decline in performance when tested with unknown domains. To resolve
the issue and obtain a generalized detection ability, we propose Bilateral
High-Pass Filters (BiHPF), which amplify the effect of the frequency-level
artifacts that are known to be found in the synthesized images of generative
models. Numerous experimental results validate that our method outperforms
other state-of-the-art methods, even when tested with unseen domains.
