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

Overview

Adaptive Segmentation Mask Attack

This repository contains the implementation of the Adaptive Segmentation Mask Attack (ASMA), a targeted adversarial example generation method for deep learning segmentation models. This attack was proposed in the paper "Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation." published in the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI-2019. (Link to the paper)

General Information

This repository is organized as follows:

  • Code - src/ folder contains necessary python files to perform the attack and calculate various stats (i.e., correctness and modification)

  • Data - data/ folder contains a couple of examples for testing purposes. The data we used in this study can be taken from [1].

  • Model - Example model used in this repository can be downloaded from https://www.dropbox.com/s/6ziz7s070kkaexp/eye_pretrained_model.pt . helper_functions.py contains a function to load this file and main.py contains an exaple that uses this model.

Frequently Asked Questions (FAQ)

  • How can I run the demo?

    1- Download the model from https://www.dropbox.com/s/6ziz7s070kkaexp/eye_pretrained_model.pt

    2- Create a folder called model on the same level as data and src, put the model under this (model) folder.

    3- Run main.py.

  • Would this attack work in multi-class segmentation models?

    Yes, given that you provide a proper target mask, model etc.

  • Does the code require any modifications in order to make it work for multi-class segmentation models?

    No (probably, depending on your model/input). At least the attack itself (adaptive_attack.py) should not require major modifications on its logic.

Citation

If you find the code in this repository useful for your research, consider citing our paper. Also, feel free to use any visuals available here.

@inproceedings{ozbulak2019impact,
    title={Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation},
    author={Ozbulak, Utku and Van Messem, Arnout and De Neve, Wesley},
    booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
    pages={300--308},
    year={2019},
    organization={Springer}
}

Requirements

python > 3.5
torch >= 0.4.0
torchvision >= 0.1.9
numpy >= 1.13.0
PIL >= 1.1.7

References

[1] Pena-Betancor C., Gonzalez-Hernandez M., Fumero-Batista F., Sigut J., Medina-Mesa E., Alayon S., Gonzalez M. Estimation of the relative amount of hemoglobin in the cup and neuroretinal rim using stereoscopic color fundus images.

[2] Ronneberger, O., Fischer, P., Brox, T. U-Net: Convolutional networks for biomedical image segmentation.

Owner
Utku Ozbulak
Fourth-year doctoral student at Ghent University. Located in Ghent University Global Campus, South Korea.
Utku Ozbulak
The VeriNet toolkit for verification of neural networks

VeriNet The VeriNet toolkit is a state-of-the-art sound and complete symbolic interval propagation based toolkit for verification of neural networks.

9 Dec 21, 2022
Image reconstruction done with untrained neural networks.

PyTorch Deep Image Prior An implementation of image reconstruction methods from Deep Image Prior (Ulyanov et al., 2017) in PyTorch. The point of the p

Atiyo Ghosh 192 Nov 30, 2022
Copy Paste positive polyp using poisson image blending for medical image segmentation

Copy Paste positive polyp using poisson image blending for medical image segmentation According poisson image blending I've completely used it for bio

Phạm Vũ Hùng 2 Oct 19, 2021
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement In this project, we proposed a Domain Disentanglement Faster-RCNN (DDF)

19 Nov 24, 2022
Code for the preprint "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and

LancoPKU 25 Dec 11, 2022
(CVPR 2022) Energy-based Latent Aligner for Incremental Learning

Energy-based Latent Aligner for Incremental Learning Accepted to CVPR 2022 We illustrate an Incremental Learning model trained on a continuum of tasks

Joseph K J 37 Jan 03, 2023
Image Segmentation with U-Net Algorithm on Carvana Dataset using AWS Sagemaker

Image Segmentation with U-Net Algorithm on Carvana Dataset using AWS Sagemaker This is a full project of image segmentation using the model built with

Htin Aung Lu 1 Jan 04, 2022
Architecture Patterns with Python (TDD, DDD, EDM)

architecture-traning Architecture Patterns with Python (TDD, DDD, EDM) Chapter 5. 높은 기어비와 낮은 기어비의 TDD 5.2 도메인 계층 테스트를 서비스 계층으로 옮겨야 하는가? 도메인 계층 테스트 def

minsung sim 2 Mar 04, 2022
Machine learning notebooks in different subjects optimized to run in google collaboratory

Notebooks Name Description Category Link Training pix2pix This notebook shows a simple pipeline for training pix2pix on a simple dataset. Most of the

Zaid Alyafeai 363 Dec 06, 2022
Tom-the-AI - A compound artificial intelligence software for Linux systems.

Tom the AI (version 0.82) WARNING: This software is not yet ready to use, I'm still setting up the GitHub repository. Should be ready in a few days. T

2 Apr 28, 2022
Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

Can Active Learning Preemptively Mitigate Fairness Issues? Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented a

ElementAI 7 Aug 12, 2022
Lightweight, Python library for fast and reproducible experimentation :microscope:

Steppy What is Steppy? Steppy is a lightweight, open-source, Python 3 library for fast and reproducible experimentation. Steppy lets data scientist fo

minerva.ml 134 Jul 10, 2022
Editing a classifier by rewriting its prediction rules

This repository contains the code and data for our paper: Editing a classifier by rewriting its prediction rules Shibani Santurkar*, Dimitris Tsipras*

Madry Lab 86 Dec 27, 2022
DIRL: Domain-Invariant Representation Learning

DIRL: Domain-Invariant Representation Learning Domain-Invariant Representation Learning (DIRL) is a novel algorithm that semantically aligns both the

Ajay Tanwani 30 Nov 07, 2022
Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021

This folder contains the code for 'Scalable Variational Approaches for Bayesian Causal Discovery'. Installation To install, use conda with conda env c

14 Sep 21, 2022
Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion (CVPR 2021)

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion (CVPR 2021) This repository is for BAAF-Net introduce

90 Dec 29, 2022
Pytorch implementation for "Implicit Semantic Response Alignment for Partial Domain Adaptation"

Implicit-Semantic-Response-Alignment Pytorch implementation for "Implicit Semantic Response Alignment for Partial Domain Adaptation" Prerequisites pyt

4 Dec 19, 2022
Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. Now with tensorflow 1.0 support. Evaluation usa

Marcel R. 349 Aug 06, 2022
The code for "Deep Level Set for Box-supervised Instance Segmentation in Aerial Images".

Deep Levelset for Box-supervised Instance Segmentation in Aerial Images Wentong Li, Yijie Chen, Wenyu Liu, Jianke Zhu* Any questions or discussions ar

sunshine.lwt 112 Jan 05, 2023
Various operations like path tracking, counting, etc by using yolov5

Object-tracing-with-YOLOv5 Various operations like path tracking, counting, etc by using yolov5

Pawan Valluri 5 Nov 28, 2022