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The advancement in numerous generative models has a two-fold effect: a simpleand easy generation of realistic synthesized images, but also an increased riskof malicious abuse of those images. Thus, it is important to develop ageneralized detector for synthesized images of any GAN model or objectcategory, including those unseen during the training phase. However, theconventional methods heavily depend on the training settings, which cause adramatic decline in performance when tested with unknown domains. To resolvethe issue and obtain a generalized detection ability, we propose BilateralHigh-Pass Filters (BiHPF), which amplify the effect of the frequency-levelartifacts that are known to be found in the synthesized images of generativemodels. Numerous experimental results validate that our method outperformsother state-of-the-art methods, even when tested with unseen domains.

BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection