Wavelet-Packet Powered Deepfake Image Detection

As neural networks become able to generate realistic artificial images, they have the potential to improve movies, music, video games and make the internet an even more creative and inspiring place. Yet, at the same time, the latest technology potentially enables new digital ways to lie. In response, the need for a diverse and reliable method toolbox arises to identify artificial images and other content. Previous work primarily relies on pixel-space CNN or the Fourier transform. To the best of our knowledge, synthesized fake image analysis and detection methods based on a multi-scale wavelet representation, which is localized in both space and frequency, have been absent thus far. This paper proposes to learn a model for the detection of synthetic images based on the wavelet-packet representation of natural and GAN-generated images. We evaluate our method on FFHQ, CelebA, and LSUN source identification problems and find improved or competitive performance. Our forensic classifier has a small network size and can be learned efficiently. Furthermore, a comparison of the wavelet coefficients from these two sources of images allows an interpretation and identifies significant differences.