semvec/pypi-packages huggingface dataset:
picflip 0.1.2
Picflip - fix your images by removing backgrounds and converting to PNG!

picflip is a terminal application that does everything you would want to do with a image, remove their bg's and convert them to png!

remove backgrounds or convert JPG, WEBP, and even SVG images to PNG's with just a couple of commands!

Usage:
  picflip remove <input_image> <output_image>
  picflip convert <input_image> <output_image>
  picflip usage
adversarialattacknn 0.1.1
A library for adversarial attacks on neural networks
Requires Python: >=3.6

# AdversarialAttackNN

A library for adversarial attacks on neural networks. This package provides several common adversarial attack methods, including FGSM, PGD, and more, to test and evaluate the robustness of deep learning models. these are all gradient attacks

## Features

- Implementations of popular adversarial attack methods (e.g., FGSM, PGD, etc.).
- Easy-to-use API for applying attacks to any PyTorch model.
- Ability to customize attack parameters (e.g., epsilon, norm, targeted vs. untargeted attacks).
- Built-in support for ensemble models.

## Installation

You can install `adversarialattacknn` directly from GitHub using `pip`:

```bash
pip install git+https://github.com/yourusername/adversarialattacknn.git
```
## Or you can install it locally by cloning the repository:

```bash
git clone https://github.com/yourusername/adversarialattacknn.git
cd adversarialattacknn
pip install -e .
```

## USAGE
### Example: 
```python
import torch
from adversarialattacknn import Attack

# Initialize the model (replace with your own model)
model = YourModel()

# Example of using the Attack class
attack = Attack(
    attack='fgsm',
    model_name='resnet18',  # or your custom model
    epsilon=0.1,
    targeted=False,
    random_start=True,
    norm='linfty',
    loss='crossentropy'
)

# Sample data (replace with actual data)
data = torch.randn((1, 3, 224, 224))  # Batch of images
label = torch.tensor([0])  # Ground-truth label

# Perform the attack
perturbation = attack(data, label)

# Apply perturbation to input data
adversarial_example = data + perturbation
```
-santhoshkumar