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Responsible Disclosure of Generative Models Using Scalable Fingerprinting

conference contribution
posted on 2023-11-29, 18:19 authored by Ning Yu, Vladislav Skripniuk, Dingfan ChenDingfan Chen, Larry S. Davis, Mario FritzMario Fritz
Over the past seven years, deep generative models have achieved a qualitatively new level of performance. Generated data has become difficult, if not impossible, to be distinguished from real data. While there are plenty of use cases that benefit from this technology, there are also strong concerns on how this new technology can be misused to spoof sensors, generate deep fakes, and enable misinformation at scale. Unfortunately, current deep fake detection methods are not sustainable, as the gap between real and fake continues to close. In contrast, our work enables a responsible disclosure of such state-of-the-art generative models, that allows model inventors to fingerprint their models, so that the generated samples containing a fingerprint can be accurately detected and attributed to a source. Our technique achieves this by an efficient and scalable ad-hoc generation of a large population of models with distinct fingerprints. Our recommended operation point uses a 128-bit fingerprint which in principle results in more than 10^36 identifiable models. Experiments show that our method fulfills key properties of a fingerprinting mechanism and achieves effectiveness in deep fake detection and attribution.

History

Preferred Citation

Ning Yu, Vladislav Skripniuk, Dingfan Chen, Larry Davis and Mario Fritz. Responsible Disclosure of Generative Models Using Scalable Fingerprinting. In: International Conference on Learning Representations (ICLR). 2022.

Primary Research Area

  • Trustworthy Information Processing

Name of Conference

International Conference on Learning Representations (ICLR)

Legacy Posted Date

2022-03-11

Open Access Type

  • Gold

BibTeX

@inproceedings{cispa_all_3584, title = "Responsible Disclosure of Generative Models Using Scalable Fingerprinting", author = "Yu, Ning and Skripniuk, Vladislav and Chen, Dingfan and Davis, Larry S. and Fritz, Mario", booktitle="{International Conference on Learning Representations (ICLR)}", year="2022", }

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