Alternatives and similar repositories for targeted_UAP_CIFAR10

Users that are interested in targeted_UAP_CIFAR10 are comparing it to the libraries listed below

• pralab / Fast-Minimum-Norm-FMN-Attack
  Foolbox implementation for NeurIPS 2021 Paper: "Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints".
  ☆25Updated 3 years ago
• jhayes14 / UAN
  Universal Adversarial Networks
  ☆33Updated 6 years ago
• AI-secure / QEBA
  Code for CVPR2020 paper QEBA: Query-Efficient Boundary-Based Blackbox Attack
  ☆32Updated 4 years ago
• Ping-C / certifiedpatchdefense
  Repository for Certified Defenses for Adversarial Patch ICLR-2020
  ☆33Updated 4 years ago
• wronnyhuang / metapoison
  Craft poisoned data using MetaPoison
  ☆51Updated 4 years ago
• JZ-LIANG / Ensemble-Adversarial-Training
  Pytorch code for ens_adv_train
  ☆15Updated 6 years ago
• nesl / adversarial_genattack
  ☆54Updated 2 years ago
• tribhuvanesh / prediction-poisoning
  Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks (ICLR '20)
  ☆32Updated 4 years ago
• inspire-group / privacy-vs-robustness
  Privacy Risks of Securing Machine Learning Models against Adversarial Examples
  ☆44Updated 5 years ago
• thu-ml / Prior-Guided-RGF
  ☆41Updated last year
• jinyuan-jia / Certify_Topk
  ☆11Updated 5 years ago
• yunyuntsai / DNN-Model-Stealing
  Code for "CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples" (NDSS 2020)
  ☆20Updated 4 years ago
• sunbelbd / invisible_backdoor_attacks
  ☆19Updated 3 years ago
• Harry24k / CW-pytorch
  A pytorch implementation of "Towards Evaluating the Robustness of Neural Networks"
  ☆57Updated 5 years ago
• nthu-datalab / On.the.Trade-off.between.Adversarial.and.Backdoor.Robustness
  Code for "On the Trade-off between Adversarial and Backdoor Robustness" (NIPS 2020)
  ☆17Updated 4 years ago
• aldahdooh / detectors_review
  ☆51Updated 3 years ago
• giladcohen / NNIF_adv_defense
  Detection of adversarial examples using influence functions and nearest neighbors
  ☆36Updated 2 years ago
• Harry24k / catastrophic-overfitting
  Understanding Catastrophic Overfitting in Single-step Adversarial Training [AAAI 2021]
  ☆27Updated 3 years ago
• superrrpotato / Defending-Neural-Backdoors-via-Generative-Distribution-Modeling
  The code is for our NeurIPS 2019 paper: https://arxiv.org/abs/1910.04749
  ☆34Updated 5 years ago
• kenny-co / sgd-uap-torch
  Universal Adversarial Perturbations (UAPs) for PyTorch
  ☆48Updated 3 years ago
• FlouriteJ / PoisonFrogs
  Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
  ☆17Updated 6 years ago
• zhuchen03 / ConvexPolytopePosioning
  ConvexPolytopePosioning
  ☆35Updated 5 years ago
• cleverhans-lab / entangled-watermark
  ☆48Updated 4 years ago
• AI-secure / Robustness-Against-Backdoor-Attacks
  RAB: Provable Robustness Against Backdoor Attacks
  ☆39Updated last year
• qizhangli / linbp-attack
  Code for our NeurIPS 2020 paper Backpropagating Linearly Improves Transferability of Adversarial Examples.
  ☆42Updated 2 years ago
• fra31 / sparse-imperceivable-attacks
  Sparse and Imperceivable Adversarial Attacks (accepted to ICCV 2019).
  ☆40Updated 4 years ago
• MadryLab / label-consistent-backdoor-code
  Code for "Label-Consistent Backdoor Attacks"
  ☆57Updated 4 years ago
• ferjad / Universal_Adversarial_Perturbation_pytorch
  A PyTorch Implementation of the UAP attack
  ☆19Updated 6 years ago
• AdelaideAuto-IDLab / Februus
  Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems
  ☆28Updated 4 years ago
• ashafahi / inceptionv3-transferLearn-poison
  Attacking a dog vs fish classification that uses transfer learning inceptionV3
  ☆70Updated 7 years ago