https://github.com/SafiyaJan/Attacking-Neural-Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
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/TSKumarage/Neural-Authorship-Attribution
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News
https://github.com/Tarzanagh/FedNest
FedNest: Federated Bilevel, Minimax, and Compositional Optimization
https://github.com/TongCHEN779/MultiMomOpt
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks
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/Trusted-AI/adversarial-robustness-toolbox
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/art/attacks/evasion/elastic_net.py
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
https://github.com/Verified-Intelligence/alpha-beta-CROWN_vnncomp2024
https://github.com/VishaalMK/VectorDefense
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/WildTangles/adversary
https://github.com/XgDuan/pytorch-adversarial-training-nonexpansive
https://github.com/YinDFY/PGD_for_targeted_attack
Towards Deep Learning Models Resistant to Adversarial Attacks
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/ada-shen/utility
https://github.com/adielashrov/trust-ai-roma-for-llm
Towards Robust LLMs: an Adversarial Robustness Measurement Framework
https://github.com/adverML/vp-for-adversarial-robustness
https://github.com/ai-secure/adversarial-glue
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models
https://github.com/alangee/FaiR-N
https://github.com/albertmillan/adversarial-training-pytorch
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/alevine0/smoothingGenGaussian
https://github.com/alirezaabdollahpour/CURE_fast_adversarial
https://github.com/alvinchangw/DCEN_CHIL2021
What it Thinks is Important is Important: Robustness Transfers through Input Gradients
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/anguyen8/sam
Adversarial Robustness as a Prior for Learned Representations
https://github.com/aounon/distributional-robustness
https://github.com/aprincemohamed/data-driven-adversarial-amc
Data-Driven Subsampling in the Presence of an Adversarial Actor
https://github.com/arobey1/advbench
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/avrahamraviv/formal-verification-of-object-detection
https://github.com/bboylyg/NAD
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
https://github.com/bethgelab/AnalysisBySynthesis
Towards the first adversarially robust neural network model on MNIST
https://github.com/bethgelab/cifar10_challenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/bethgelab/foolbox
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.