https://github.com/Ahmadreza-Jeddi/Learn2Perturb
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness
Papers with Code 아카이브
Adversarial Robustness
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AugustineCha/pytorch-adversarial-training-master
https://github.com/BililiCode/ARGD
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
https://github.com/BjoernNieth/LS-Dataset-pruning-in-AT
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/ChaoningZhang/Dual-temperature
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
https://github.com/Cody2333/WassNet
advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
https://github.com/DIG-Beihang/RobustART
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
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/F-Salehi/CURE_robustness
https://github.com/FPNAS/LBGAT
https://github.com/FlorianMerkle/network-pruning-and-robustness
https://github.com/GraphReshape/GraphReshape
Power up! Robust Graph Convolutional Network via Graph Powering
https://github.com/Hadisalman/robust-verify-benchmark
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/HanxunH/XTransferBench
Universal Adversarial Perturbations for Vision-Language Pre-trained Models
https://github.com/Human-AI-Interaction-Projects/CaptchaGAN
https://github.com/IBM/AIF360
https://github.com/IBM/EAD-Attack
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
https://github.com/IBM/claimed
CLAIMED, a visual and scalable component library for Trusted AI
https://github.com/Jeffkang-94/pytorch-adversarial-attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/KnowledgeDiscovery/FaceSec
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Koukyosyumei/NAIST-FedML-Experiments
https://github.com/Line290/FeatureAttack
Improving Adversarial Robustness via Guided Complement Entropy
https://github.com/MILES-PSL/Adversarial-Robustness-Through-Randomization
Theoretical evidence for adversarial robustness through randomization
https://github.com/MKYucel/zero_shot_corruption_benchmarks
A Deep Dive into Adversarial Robustness in Zero-Shot Learning
https://github.com/Madanfeng/advGAN_tf2
https://github.com/MadryLab/robust-features-code
https://github.com/MadryLab/robust_representations
Adversarial Robustness as a Prior for Learned Representations
https://github.com/MadryLab/robustness_applications
https://github.com/MahsaPaknezhad/AdversariallyRobustTraining
Explaining Adversarial Vulnerability with a Data Sparsity Hypothesis
https://github.com/QData/TextAttack/blob/master/docs/2notebook/Example_6_Chinese_Attack.ipynb
Expanding Scope: Adapting English Adversarial Attacks to Chinese
https://github.com/RaphaelOlivier/gard_eval2_public
https://github.com/RogerDeng/FinancialVision
Adversarial Robustness of Deep Convolutional Candlestick Learner
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.