  Generative adversarial networks (GANs) have made remarkable progress in
synthesizing realistic-looking images that effectively outsmart even humans.
Although several detection methods can recognize these deep fakes by checking
for image artifacts from the generation process, multiple counterattacks have
demonstrated their limitations. These attacks, however, still require certain
conditions to hold, such as interacting with the detection method or adjusting
the GAN directly. In this paper, we introduce a novel class of simple
counterattacks that overcomes these limitations. In particular, we show that an
adversary can remove indicative artifacts, the GAN fingerprint, directly from
the frequency spectrum of a generated image. We explore different realizations
of this removal, ranging from filtering high frequencies to more nuanced
frequency-peak cleansing. We evaluate the performance of our attack with
different detection methods, GAN architectures, and datasets. Our results show
that an adversary can often remove GAN fingerprints and thus evade the
detection of generated images.
