Title: Artificial GAN Fingerprints: Rooting Deepfake Attribution in Training Data. Abstract: Photorealistic image generation is progressing rapidly and has reached a new level of quality, thanks to the invention and breakthroughs of generative adversarial networks (GANs). Yet the dark side of such $\textit{deepfakes}$, the malicious use of generated media, never stops raising concerns of visual misinformation. Existing research works on deepfake detection demonstrate impressive accuracy, while it is accompanied by adversarial iterations on detection countermeasure techniques. In order to lead this arms race to the end, we investigate a fundamental solution on deepfake detection, agnostic to the evolution of GANs in order to enable a responsible disclosure or regulation of such double-edged techniques. We propose to embed $\textbf{artificial fingerprints}$ into GAN training data, and show a surprising discovery on the $\textbf{transferability}$ of such fingerprints from training data to GAN models, which in turn enables reliable detection and attribution of deepfakes. Our empirical study shows that our fingerprinting technique (1) holds for different state-of-the-art GAN configurations, (2) turns more effective along with the development of GAN techniques, (3) has a negligible side effect on the generation quality, and (4) stays robust against image-level and model-level perturbations. When we allocate each GAN publisher a unique artificial fingerprint, the margins between real data and deepfakes, and the margins among different deepfake sources are fundamentally guaranteed. As a result, we are able to evidence accurate deepfake detection/attribution using our fingerprint decoder, which makes this solution stand out from the current arms race.