Papers with Code 아카이브
Adversarial Attack
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 3
- 보존된 평가 기록
- 9
An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.
Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Annonymous-repos/attacks-in-pytorch
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
https://github.com/ArashRahnama/Adversarial-Explainations-for-Artificial-Intelligence-systems-AXAI
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
https://github.com/ArashRahnama/Adversarial-Explanations-for-Artificial-Intelligence-Systems-AXAI
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
https://github.com/B-Yassine/Carlini-and-Wagner_InceptionV3_Imagenet
https://github.com/BaoWangMath/Graph-Structured-Recurrent-Neural-Nets-
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
https://github.com/BeyondCloud/Comp04_ReverseImageCaption
https://github.com/Bhushan-Jagtap-2013/Adversarial_Attack_on_RNN
https://github.com/BililiCode/ARGD
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
https://github.com/BorealisAI/advertorch/blob/master/advertorch/attacks/ead.py
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
https://github.com/Cadden/paddle-adver
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Caimthefool/Paddle_T2I
https://github.com/Carco-git/CW_Attack_on_MNIST
https://github.com/ChaoningZhang/Awesome-Universal-Adversarial-Perturbations
https://github.com/Cody2333/WassNet
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
https://github.com/CorneliusHsiao/FoodMethodGAN
https://github.com/DanielLongo/AdversarialTrain
https://github.com/DanielLongo/GANs
https://github.com/DanielLongo/cGANs
https://github.com/DentanJeremie/adversarialTransferts
Task-generalizable Adversarial Attack based on Perceptual Metric
https://github.com/DerryHub/ByteTrack-attack
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers
https://github.com/DreamtaleCore/Refool
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
https://github.com/EACN2022/EACN
https://github.com/EPFL-VILAB/XDEnsembles
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/ETH-DISCO/self-ensembling
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness
https://github.com/ElisaNguyen/dl-defense-pgd
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
https://github.com/EvgeniaAR/foolbox
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
https://github.com/FAKEBOB-adversarial-attack/FAKEBOB
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
https://github.com/FeiLiu36/LLM4MOEA
L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks
https://github.com/FenHua/transfer_adv
Improving Adversarial Transferability with Gradient Refining
https://github.com/FlorentF9/DeepTemporalClustering
https://github.com/Gorilla-Lab-SCUT/GeoA3
https://github.com/GraphReshape/GraphReshape
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
https://github.com/Guang000/Awesome-Dataset-Distillation
https://github.com/Hadisalman/robust-verify-benchmark
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Hanjun-Dai/graph_adversarial_attack
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.