Decentralized Attribution of Generative Models
arXiv:2010.13974
Abstract
Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement. One solution to these threats is model attribution, i.e., the identification of user-end models where the contents under question are generated from. Existing studies showed empirical feasibility of attribution through a centralized classifier trained on all user-end models. However, this approach is not scalable in reality as the number of models ever grows. Neither does it provide an attributability guarantee. To this end, this paper studies decentralized attribution, which relies on binary classifiers associated with each user-end model. Each binary classifier is parameterized by a user-specific key and distinguishes its associated model distribution from the authentic data distribution. We develop sufficient conditions of the keys that guarantee an attributability lower bound. Our method is validated on MNIST, CelebA, and FFHQ datasets. We also examine the trade-off between generation quality and robustness of attribution against adversarial post-processes.
arXiv comment16 pages, 7 figures
References in corpus (15)
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- Spectral Normalization for Generative Adversarial Networks
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- CTRL: A Conditional Transformer Language Model for Controllable Generation
- Embedding Watermarks into Deep Neural Networks
- Adversarial Frontier Stitching for Remote Neural Network Watermarking
- The sphere packing problem in dimension 24
- Video-to-Video Synthesis
- StarGAN v2: Diverse Image Synthesis for Multiple Domains
- SteganoGAN: High Capacity Image Steganography with GANs
- DAWN: Dynamic Adversarial Watermarking of Neural Networks
- Detecting and Simulating Artifacts in GAN Fake Images
- Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints