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README.md

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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. 
+
+![Architecture_Base](images/camouflage-base.jpg)
+
+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.
+
+![Architecture_Light](images/camouflage-light.jpg)
+
+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. 
+
+![Architecture-Face](images/camouflage-face.jpg)
+
+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}
+}
+```
+