  CNN-based generative modelling has evolved to produce synthetic images
indistinguishable from real images in the RGB pixel space. Recent works have
observed that CNN-generated images share a systematic shortcoming in
replicating high frequency Fourier spectrum decay attributes. Furthermore,
these works have successfully exploited this systematic shortcoming to detect
CNN-generated images reporting up to 99% accuracy across multiple
state-of-the-art GAN models.
  In this work, we investigate the validity of assertions claiming that
CNN-generated images are unable to achieve high frequency spectral decay
consistency. We meticulously construct a counterexample space of high frequency
spectral decay consistent CNN-generated images emerging from our handcrafted
experiments using DCGAN, LSGAN, WGAN-GP and StarGAN, where we empirically show
that this frequency discrepancy can be avoided by a minor architecture change
in the last upsampling operation. We subsequently use images from this
counterexample space to successfully bypass the recently proposed forensics
detector which leverages on high frequency Fourier spectrum decay attributes
for CNN-generated image detection.
  Through this study, we show that high frequency Fourier spectrum decay
discrepancies are not inherent characteristics for existing CNN-based
generative models--contrary to the belief of some existing work--, and such
features are not robust to perform synthetic image detection. Our results
prompt re-thinking of using high frequency Fourier spectrum decay attributes
for CNN-generated image detection. Code and models are available at
https://keshik6.github.io/Fourier-Discrepancies-CNN-Detection/
