  The current high-fidelity generation and high-precision detection of DeepFake
images are at an arms race. We believe that producing DeepFakes that are highly
realistic and 'detection evasive' can serve the ultimate goal of improving
future generation DeepFake detection capabilities. In this paper, we propose a
simple yet powerful pipeline to reduce the artifact patterns of fake images
without hurting image quality by performing implicit spatial-domain notch
filtering. We first demonstrate that frequency-domain notch filtering, although
famously shown to be effective in removing periodic noise in the spatial
domain, is infeasible for our task at hand due to the manual designs required
for the notch filters. We, therefore, resort to a learning-based approach to
reproduce the notch filtering effects, but solely in the spatial domain. We
adopt a combination of adding overwhelming spatial noise for breaking the
periodic noise pattern and deep image filtering to reconstruct the noise-free
fake images, and we name our method DeepNotch. Deep image filtering provides a
specialized filter for each pixel in the noisy image, producing filtered images
with high fidelity compared to their DeepFake counterparts. Moreover, we also
use the semantic information of the image to generate an adversarial guidance
map to add noise intelligently. Our large-scale evaluation on 3 representative
state-of-the-art DeepFake detection methods (tested on 16 types of DeepFakes)
has demonstrated that our technique significantly reduces the accuracy of these
3 fake image detection methods, 36.79% on average and up to 97.02% in the best
case.
