Defending against Model Stealing via Verifying Embedded External Features

Overview

Defending against Model Stealing Attacks via Verifying Embedded External Features

This is the official implementation of our paper Defending against Model Stealing Attacks via Verifying Embedded External Features, accepted by the AAAI Conference on Artificial Intelligence (AAAI), 2022. This research project is developed based on Python 3 and Pytorch, created by Yiming Li and Linghui Zhu.

Pipeline

Pipeline

Requirements

To install requirements:

pip install -r requirements.txt

Make sure the directory follows:

stealingverification
├── data
│   ├── cifar10
│   └── ...
├── gradients_set 
│   
├── prob
│   
├── network
│   
├── model
│   ├── victim
│   └── ...
|

Dataset Preparation

Make sure the directory data follows:

data
├── cifar10_seurat_10%
|   ├── train
│   └── test
├── cifar10  
│   ├── train
│   └── test
├── subimage_seurat_10%
│   ├── train
|   ├── val
│   └── test
├── sub-imagenet-20
│   ├── train
|   ├── val
│   └── test

📋 Data Download Link:
data

Model Preparation

Make sure the directory model follows:

model
├── victim
│   ├── vict-wrn28-10.pt
│   └── ...
├── benign
│   ├── benign-wrn28-10.pt
│   └── ...
├── attack
│   ├── atta-label-wrn16-1.pt
│   └── ...
└── clf

📋 Model Download Link:
model

Collecting Gradient Vectors

Collect gradient vectors of victim and benign model with respect to transformed images.

CIFAR-10:

python gradientset.py --model=wrn16-1 --m=./model/victim/vict-wrn16-1.pt --dataset=cifar10 --gpu=0
python gradientset.py --model=wrn28-10 --m=./model/victim/vict-wrn28-10.pt --dataset=cifar10 --gpu=0
python gradientset.py --model=wrn16-1 --m=./model/benign/benign-wrn16-1.pt --dataset=cifar10 --gpu=0
python gradientset.py --model=wrn28-10 --m=./model/benign/benign-wrn28-10.pt --dataset=cifar10 --gpu=0

ImageNet:

python gradientset.py --model=resnet34-imgnet --m=./model/victim/vict-imgnet-resnet34.pt --dataset=imagenet --gpu=0
python gradientset.py --model=resnet18-imgnet --m=./model/victim/vict-imgnet-resnet18.pt --dataset=imagenet --gpu=0
python gradientset.py --model=resnet34-imgnet --m=./model/benign/benign-imgnet-resnet34.pt --dataset=imagenet --gpu=0
python gradientset.py --model=resnet18-imgnet --m=./model/benign/benign-imgnet-resnet18.pt --dataset=imagenet --gpu=0

Training Ownership Meta-Classifier

To train the ownership meta-classifier in the paper, run these commands:

CIFAR-10:

python train_clf.py --type=wrn28-10 --dataset=cifar10 --gpu=0
python train_clf.py --type=wrn16-1 --dataset=cifar10 --gpu=0

ImageNet:

python train_clf.py --type=resnet34-imgnet --dataset=imagenet --gpu=0
python train_clf.py --type=resnet18-imgnet --dataset=imagenet --gpu=0

Ownership Verification

To verify the ownership of the suspicious models, run this command:

CIFAR-10:

python ownership_verification.py --mode=source --dataset=cifar10 --gpu=0 

#mode: ['source','distillation','zero-shot','fine-tune','label-query','logit-query','benign']

ImageNet:

python ownership_verification.py --mode=logit-query --dataset=imagenet --gpu=0 

#mode: ['source','distillation','zero-shot','fine-tune','label-query','logit-query','benign']

An Example of the Result

python ownership_verification.py --mode=fine-tune --dataset=cifar10 --gpu=0 

result:  p-val: 1.9594572166549425e-08 mu: 0.47074130177497864

Reference

If our work or this repo is useful for your research, please cite our paper as follows:

@inproceedings{li2022defending,
  title={Defending against Model Stealing via Verifying Embedded External Features},
  author={Li, Yiming and Zhu, Linghui and Jia, Xiaojun and Jiang, Yong and Xia, Shu-Tao and Cao, Xiaochun},
  booktitle={AAAI},
  year={2022}
}
HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

HugsVision is an open-source and easy to use all-in-one huggingface wrapper for computer vision. The goal is to create a fast, flexible and user-frien

Labrak Yanis 166 Nov 27, 2022
Semantic similarity computation with different state-of-the-art metrics

Semantic similarity computation with different state-of-the-art metrics Description • Installation • Usage • License Description TaxoSS is a semantic

6 Jun 22, 2022
Object-aware Contrastive Learning for Debiased Scene Representation

Object-aware Contrastive Learning Official PyTorch implementation of "Object-aware Contrastive Learning for Debiased Scene Representation" by Sangwoo

43 Dec 14, 2022
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach (CVPR2021)

Monocular 3D Object Detection: An Extrinsic Parameter Free Approach (CVPR2021) Yunsong Zhou, Yuan He, Hongzi Zhu, Cheng Wang, Hongyang Li, Qinhong Jia

Yunsong Zhou 51 Dec 14, 2022
CVPR 2021 Challenge on Super-Resolution Space

Learning the Super-Resolution Space Challenge NTIRE 2021 at CVPR Learning the Super-Resolution Space challenge is held as a part of the 6th edition of

andreas 104 Oct 26, 2022
The code for our paper Semi-Supervised Learning with Multi-Head Co-Training

Semi-Supervised Learning with Multi-Head Co-Training (PyTorch) Abstract Co-training, extended from self-training, is one of the frameworks for semi-su

cmc 6 Dec 04, 2022
Py-FEAT: Python Facial Expression Analysis Toolbox

Py-FEAT is a suite for facial expressions (FEX) research written in Python. This package includes tools to detect faces, extract emotional facial expressions (e.g., happiness, sadness, anger), facial

Computational Social Affective Neuroscience Laboratory 147 Jan 06, 2023
DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks

English | 简体中文 Introduction DeepHawkeye is a library to detect unusual patterns in images using features from pretrained neural networks Reference Pat

CV Newbie 28 Dec 13, 2022
An implementation for the ICCV 2021 paper Deep Permutation Equivariant Structure from Motion.

Deep Permutation Equivariant Structure from Motion Paper | Poster This repository contains an implementation for the ICCV 2021 paper Deep Permutation

72 Dec 27, 2022
BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation

BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation Installing The Dependencies $ conda create --name beametrics python

7 Jul 04, 2022
Source code for "MusCaps: Generating Captions for Music Audio" (IJCNN 2021)

MusCaps: Generating Captions for Music Audio Ilaria Manco1 2, Emmanouil Benetos1, Elio Quinton2, Gyorgy Fazekas1 1 Queen Mary University of London, 2

Ilaria Manco 57 Dec 07, 2022
Vector.ai assignment

fabio-tests-nisargatman Low Level Approach: ###Tables: continents: id*, name, population, area, createdAt, updatedAt countries: id*, name, population,

Ravi Pullagurla 1 Nov 09, 2021
PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"

Adam-NSCL This is a PyTorch implementation of Adam-NSCL algorithm for continual learning from our CVPR2021 (oral) paper: Title: Training Networks in N

Shipeng Wang 34 Dec 21, 2022
Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning

Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning

Ian Pointer 368 Dec 17, 2022
Riemannian Geometry for Molecular Surface Approximation (RGMolSA)

Riemannian Geometry for Molecular Surface Approximation (RGMolSA) Introduction Ligand-based virtual screening aims to reduce the cost and duration of

11 Nov 15, 2022
Disentangled Face Attribute Editing via Instance-Aware Latent Space Search, accepted by IJCAI 2021.

Instance-Aware Latent-Space Search This is a PyTorch implementation of the following paper: Disentangled Face Attribute Editing via Instance-Aware Lat

67 Dec 21, 2022
Randomizes the warps in a stock pokeemerald repo.

pokeemerald warp randomizer Randomizes the warps in a stock pokeemerald repo. Usage Instructions Install networkx and matplotlib via pip3 or similar.

Max Thomas 6 Mar 17, 2022
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

YeongHyeon Park 7 Aug 28, 2022
Bu repo SAHI uygulamasını mantığını öğreniyoruz.

SAHI-Learn: SAHI'den Beraber Kodlamak İster Misiniz Herkese merhabalar ben Kadir Nar. SAHI kütüphanesine gönüllü geliştiriciyim. Bu repo SAHI kütüphan

Kadir Nar 11 Aug 22, 2022
DTCN IJCAI - Sequential prediction learning framework and algorithm

DTCN This is the implementation of our paper "Sequential Prediction of Social Me

Bobby 2 Jan 24, 2022