| # 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} | |
| } | |
| ``` | |