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# CAMOUFLaGE: Controllable AnoniMizatiOn throUgh diFfusion-based image coLlection GEneration
Code [Here](https://gitlab.com/grains2/camouflage)
Official implementations of ["Latent Diffusion Models for Attribute-Preserving Image Anonymization"](#latent-diffusion-models-for-attribute-preserving-image-anonymization)
and ["Harnessing Foundation Models for Image Anonymization"](#harnessing-foundation-models-for-image-anonymization).
## Latent Diffusion Models for Attribute-Preserving Image Anonymization
[[Paper]](https://arxiv.org/abs/2403.14790)
This paper presents, to the best of our knowledge, the first approach to image anonymization based on
Latent Diffusion Models (LDMs). Every element of a scene is maintained to convey the same meaning, yet
manipulated in a way that makes re-identification difficult. We propose two LDMs for this purpose:
- *CAMOUFLaGE-Base*
- *CAMOFULaGE-Light*
The former solution achieves superior performance on most metrics and benchmarks, while the latter cuts
the inference time in half at the cost of fine-tuning a lightweight module.
Compared to state-of-the-art, we anonymize complex scenes by introducing variations in the faces, bodies,
and background elements.
#### CAMOUFLaGE-Base
CAMOUFLaGE-Base exploits a combination of pre-trained ControlNets and introduces an anonymizazion guidance based on
the original image.
More details on its usage can be found [here](CAMOUFLaGE-Base-v1-0).
#### CAMOUFLaGE-Light
CAMOUFLaGE-Light trains a lightweight IP-Adapter to encode key elements of the scene and facial attributes of each
person.
More details on its usage can be found [here](CAMOUFLaGE_light).
## Harnessing Foundation Models for Image Anonymization
[[Paper]]()
We explore how foundation models can be leveraged to solve tasks, specifically focusing on anonymization,
without the requirement for training or fine-tuning. By bypassing traditional pipelines, we demonstrate the
efficiency and effectiveness of this approach in achieving anonymization objectives directly from the
foundation model’s inherent knowledge.
#### CAMOUFLaGE-Face
We examine how foundation models can generate anonymized images directly from textual descriptions. Two models
were employed for information extraction: FACER, used to identify the 40 CelebA-HQ attributes, and DeepFace,
used to determine ethnicity and age. Using this rich information, we craft captions to guide the generation process.
Classifier-free guidance was employed to push the image content in the direction of the positive prompt P and far
from the negative prompt ¬P.
More details on its usage can be found [here](GEM2024).
## Citation
If you find CAMOUFLaGE-Base and/or CAMOUFLaGE-Light useful, please cite:
```
@misc{camouflage,
title={Latent Diffusion Models for Attribute-Preserving Image Anonymization},
author={Luca Piano and Pietro Basci and Fabrizio Lamberti and Lia Morra},
year={2024},
eprint={2403.14790},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
If you find CAMOUFLaGE-Face useful, please cite:
```
@inproceedings{pianoGEM24,
title={Harnessing Foundation Models for Image Anonymization},
author={Piano, Luca and Basci, Pietro and Lamberti, Fabrizio and Morra, Lia},
booktitle={2024 IEEE CTSoc Gaming, Entertainment and Media},
year={2024},
organization={IEEE}
}
```
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