EKILA: Synthetic Media Provenance and Attribution for Generative Art-Reference-Cited by-同舟云学术

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EKILA: Synthetic Media Provenance and Attribution for Generative Art

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Author:

Balan Kar¹,Agarwal Shruti²,Jenni Simon²,Parsons Andy²,Gilbert Andrew¹,Collomosse John¹

Affiliation:

1. University of Surrey

2. Adobe Inc.

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10208270/10208119/10208679.pdf?arnumber=10208679

Reference56 articles.

1. A fragile watermarking scheme for image authentication with tamper localization using integer wavelet transform;devi;J Computer Science,2019

2. Coalition for Content Provenance and Authenticity;Draft technical specification 0 7 Technical report C2PA,2021

3. WildDeepfake

4. The deepfake detection challenge (DFDC) dataset;dolhansky;CoRR,2020

5. Diffusion models beat gans on image synthesis;dhariwal,2021

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1. ORAgen: Exploring the Design of Attribution through Media Tokenisation;Designing Interactive Systems Conference;2024-07

2. To Authenticity, and Beyond! Building Safe and Fair Generative AI Upon the Three Pillars of Provenance;IEEE Computer Graphics and Applications;2024-05

3. GenerativeGI: creating generative art with genetic improvement;Automated Software Engineering;2024-03-01

4. DeepMint: Non-fungible Token Generation Using Deep Learning;Innovations in Sustainable Technologies and Computing;2024

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