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Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data

conference contribution
posted on 2023-11-29, 18:23 authored by Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi, Mario FritzMario Fritz
Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs). Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. While existing research work on deepfake detection demonstrates high accuracy, it is subject to advances in generation techniques and adversarial iterations on detection countermeasure techniques. Thus, we seek a proactive and sustainable solution on deepfake detection, that is agnostic to the evolution of generative models, by introducing artificial fingerprints into the models. Our approach is simple and effective. We first embed artificial fingerprints into training data, then validate a surprising discovery on the transferability of such fingerprints from training data to generative models, which in turn appears in the generated deepfakes. Experiments show that our fingerprinting solution (1) holds for a variety of cutting-edge generative models, (2) leads to a negligible side effect on generation quality, (3) stays robust against image-level and model-level perturbations, (4) stays hard to be detected by adversaries, and (5) converts deepfake detection and attribution into trivial tasks and outperforms the recent state-of-the-art baselines. Our solution closes the responsibility loop between publishing pre-trained generative model inventions and their possible misuses, which makes it independent of the current arms race.

History

Preferred Citation

Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi and Mario Fritz. Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data. In: IEEE International Conference on Computer Vision (ICCV). 2020.

Primary Research Area

  • Trustworthy Information Processing

Secondary Research Area

  • Threat Detection and Defenses

Name of Conference

IEEE International Conference on Computer Vision (ICCV)

Legacy Posted Date

2020-07-17

Book Title

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Page Range

14428-14437

Open Access Type

  • Gold

BibTeX

@inproceedings{cispa_all_3155, title = "Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data", author = "Yu, Ning and Skripniuk, Vladislav and Abdelnabi, Sahar and Fritz, Mario", booktitle="{IEEE International Conference on Computer Vision (ICCV)}", year="2020", }

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