1 research outputs found
Conditional Adversarial Camera Model Anonymization
The model of camera that was used to capture a particular photographic image
(model attribution) is typically inferred from high-frequency model-specific
artifacts present within the image. Model anonymization is the process of
transforming these artifacts such that the apparent capture model is changed.
We propose a conditional adversarial approach for learning such
transformations. In contrast to previous works, we cast model anonymization as
the process of transforming both high and low spatial frequency information. We
augment the objective with the loss from a pre-trained dual-stream model
attribution classifier, which constrains the generative network to transform
the full range of artifacts. Quantitative comparisons demonstrate the efficacy
of our framework in a restrictive non-interactive black-box setting.Comment: ECCV 2020 - Advances in Image Manipulation workshop (AIM 2020