Papers with Code 아카이브

Adversarial Defense

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

10

보존된 평가 기록

28

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/RaphaelOlivier/gard_eval2_public

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/Recognito-Vision/Face-SDK-Linux-Demos

AdvFAS: A robust face anti-spoofing framework against adversarial examples

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

Sample Correlation for Fingerprinting Deep Face Recognition

https://github.com/Roy-YL/VAE-Adversarial-Defense

Adversarial Defense of Image Classification Using a Variational Auto-Encoder

https://github.com/SCUTjinchengli/DefenseTransformer

Learning Defense Transformers for Counterattacking Adversarial Examples

https://github.com/SafiyaJan/Attacking-Neural-Networks

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/TonyYaoMSU/ProjectedGradientDescent

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/TonyYaoMSU/TRADES

Theoretically Principled Trade-off between Robustness and Accuracy

https://github.com/UnofficialJuliaMirrorSnapshots/MIPVerify.jl-e5e5f8be-2a6a-5994-adbb-5afbd0e30425

Certified Defenses against Adversarial Examples

https://github.com/VishaalMK/VectorDefense

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/YinDFY/PGD_for_targeted_attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/YutingLi0606/SURE

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/Zoky-2020/Set-level_Guidance_Attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/abahram77/mnistChallenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/abahram77/mnist_challenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/adverML/GuidedDiffusionPur

Guided Diffusion Model for Adversarial Purification

https://github.com/adverML/vp-for-adversarial-robustness

Visual Prompting for Adversarial Robustness

https://github.com/akshaymehra24/poisoning_certified_defenses

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/albertmillan/adversarial-training-pytorch

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/alevine0/smoothingGenGaussian

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/allenai/robustnav

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/amerch/CIFAR100-Training

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/andrewilyas/ens-adv-train-attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/anirudh9784/Adversarial-Defense

An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks

https://github.com/anishathalye/Guided-Denoise

Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser

https://github.com/aounon/distributional-robustness

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/arm-software/ml-restructurable-activation-networks

Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge

https://github.com/arobey1/advbench

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/arobey1/mbrdl

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/as791/NOMARO_defense

https://github.com/as791/ZOO_Attack_PyTorch

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

https://github.com/automl/nes

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/azshue/boosting_robust

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

https://github.com/bethgelab/cifar10_challenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/bethgelab/imagecorruptions

Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

https://github.com/bibin-sebastian/Physical_Adversarial_examples_GAN

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.