IdProv: Identity-Based Provenance for Synthetic Image Generation (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v37i13.26942Keywords:
Identity Leakage, Generative Adversarial Networks (GANs), Synthetic Face Images, Image ProvenanceAbstract
Recent advancements in Generative Adversarial Networks (GANs) have made it possible to obtain high-quality face images of synthetic identities. These networks see large amounts of real faces in order to learn to generate realistic looking synthetic images. However, the concept of a synthetic identity for these images is not very well-defined. In this work, we verify identity leakage from the training set containing real images into the latent space and propose a novel method, IdProv, that uses image composition to trace the source of identity signals in the generated image.Downloads
Published
2023-09-06
How to Cite
Bhatia, H., Singh, J., Sangwan, G., Bharati, A., Singh, R., & Vatsa, M. (2023). IdProv: Identity-Based Provenance for Synthetic Image Generation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16164-16165. https://doi.org/10.1609/aaai.v37i13.26942
Issue
Section
AAAI Student Abstract and Poster Program