utkuozbulak/regional-adversarial-perturbation utkuozbulak/regional-adversarial-perturbation

  • Stars
    star
    6
  • Rank 2,458,056 (Top 50 %)
  • Language
    Python
  • License
    MIT License
  • Created almost 4 years ago
  • Updated almost 4 years ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
utkuozbulak/regional-adversarial-perturbation
github

utkuozbulak

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Code and materials for the paper "Regional Image Perturbation Reduces Lp Norms of Adversarial Examples While Maintaining Model-to-model Transferability" (ICML 2020, UDL).

More Repositories

1

pytorch-cnn-visualizations

Pytorch implementation of convolutional neural network visualization techniques
Python
7,631
star
2

pytorch-custom-dataset-examples

Some custom dataset examples for PyTorch
Python
857
star
3

pytorch-cnn-adversarial-attacks

Pytorch implementation of convolutional neural network adversarial attack techniques
Python
344
star
4

unsupervised-learning-document-clustering

Document clustering and topic modelling with Python
Python
85
star
5

adaptive-segmentation-mask-attack

Pre-trained model, code, and materials from the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation" (MICCAI 2019).
Python
55
star
6

facial-expression-recognition

Predicting facial expressions with machine learning algorithms
Python
48
star
7

singular-value-decomposition-on-images

Singular Value Decomposition on rgb and grayscale images
Python
12
star
8

imagenet-adversarial-image-evaluation

Code and some materials from the papers "Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks" (BMVC 2021) and "Evaluating Adversarial Attacks on ImageNet:A Reality Check on Misclassification Classes" (NeurIPS 2021, Workshop track).
Python
11
star
9

UCL-DSSC-Hackaton

Winning project of 2017 University College London Data Science Hackathon
Jupyter Notebook
7
star
10

mutate-and-observe

Materials for the paper (Bioinformatics) "Mutate and Observe: Utilizing Deep Neural Networks to Investigate the Impact of Mutations on Translation Initiation"
Python
3
star
11

cnn-gifs

1
star