Geminio

(★ 9)

[ICCV 2025] Geminio is a VLM-powered gradient inversion attack in federated learning (FL). It allows the adversary (the FL server) to describe the data of value and reconstruct the victim client's private data matching the description.

Owner Repos

Imperio ★ 44

[IJCAI 2024] Imperio is an LLM-powered backdoor attack. It allows the adversary to issue language-guided instructions to control the victim model's prediction for arbitrary targets.

Python
Protego ★ 10

[CVPR 2026] Protego is a user-centric privacy protection method that safeguards facial images from retrieval-based privacy intrusions.

Jupyter Notebook
AnywhereDoor ★ 9

AnywhereDoor is a multi-target backdoor attack tailored for object detection. Once implanted, it enables adversaries to specify different attack types (object vanishing, fabrication, or misclassification) and configurations (untargeted or targeted with specific classes) to dynamically control detection behavior.

Jupyter Notebook
Confundo ★ 9

[SEC 2026] Confundo: Learning to Generate Robust Poison for Practical RAG Systems

Python

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  • .gitignore
  • LICENSE
  • main_breaching-imagenet.py
  • main_geminio-imagenet.py
  • README.md
  • reconstruct.py
  • requirements.txt
  • test_pipeline_imagenet.sh
  • vlm-imagenet-embed.py

# Use via CDN

jsDelivr

jsDelivr serves any public GitHub repository as a CDN with zero setup. Pick a version and a file to get a ready-to-paste link and snippet.

Link
Example

Command Glossary

Commands referenced in this DOCs, explained below.

🔍

conda activate

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Activate a conda environment.
See also: `conda deactivate`.

conda activate myenv

Activate an existing environment named `myenv`:

conda activate {{path/to/myenv}}

Activate an existing environment located at custom path:

conda activate --stack myenv

Stack `myenv` environment on top of a previous environment making libraries/commands/variables from both accessible:

🔍

conda create

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Create new conda environments.

conda create {{[-y|--yes]}} {{[-n|--name]}} py39 python=3.9 "numpy>=1.11" scipy

Create a new environment named `py39`, install Python 3.9, NumPy v1.11 or above in it, and the latest stable version of SciPy. Say yes to all confirmations:

conda create {{[-n|--name]}} myenv --file {{file1.yml}} --file {{file2.yml}}

Create a new environment named `myenv` and install packages listed in files:

conda create {{[-p|--prefix]}} {{path/to/myenv}}

Create a new environment at a custom path (i.e. prefix):

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pip install

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Install Python packages.

pip install {{package1 package2 ...}}

Install one or more packages:

pip install {{package1 package2 ...}} {{[-U|--upgrade]}}

Upgrade all specified packages to the latest version, installing any that are not already present:

pip install {{package}}=={{version}}

Install a specific version of a package:

🔍

python

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Python language interpreter.

python

Start a REPL (interactive shell):

python {{path/to/file.py}}

Execute a specific Python file:

python -i {{path/to/file.py}}

Execute a specific Python file and start a REPL:

# Project Badges

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// repository documentation