Ning Yu (PhD Student)

MSc Ning Yu

Max-Planck-Institut für Informatik
Saarland Informatics Campus
Campus E1 4
66123 Saarbrücken
E1 4 - Room 626
+49 681 9325 2026
+49 681 9325 2099
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Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture
N. Yu, C. Barnes, E. Shechtman, S. Amirghodsi and M. Lukáč
32nd IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019), 2019
(Accepted/in press)
Learning GAN fingerprints towards Image Attribution
N. Yu, L. Davis and M. Fritz
Technical Report, 2019
(arXiv: 1811.08180)
Recent advances in Generative Adversarial Networks (GANs) have shown increasing success in generating photorealistic images. But they also raise challenges to visual forensics and model authentication. We present the first study of learning GAN fingerprints towards image attribution: we systematically investigate the performance of classifying an image as real or GAN-generated. For GAN-generated images, we further identify their sources. Our experiments validate that GANs carry distinct model fingerprints and leave stable fingerprints to their generated images, which support image attribution. Even a single difference in GAN training initialization can result in different fingerprints, which enables fine-grained model authentication. We further validate such a fingerprint is omnipresent in different image components and is not biased by GAN artifacts. Fingerprint finetuning is effective in immunizing five types of adversarial image perturbations. Comparisons also show our learned fingerprints consistently outperform several baselines in a variety of setups.