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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/louis2889184/pytorch-adversarial-training
https://github.com/luizgh/adversarial_signatures
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/lukas-schott/AnalysisBySynthesis
Towards the first adversarially robust neural network model on MNIST
https://github.com/lynfi/ADP
Improving Adversarial Robustness via Promoting Ensemble Diversity
https://github.com/maremun/GenAttackMCS2018
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
https://github.com/matanbt/attack-tabular
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/mc-nya/fednest
FedNest: Federated Bilevel, Minimax, and Compositional Optimization
https://github.com/memoatwit/universalperturbation
Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition
https://github.com/mshane911/NRP
https://github.com/msingh27/LAT_adversarial_robustness
https://github.com/mvuyyuru/adversary
https://github.com/mwojnars/nifty
https://github.com/ndb796/pytorch-adversarial-training-cifar
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/niharikajainn/adv_gan_keras
https://github.com/nilspwalter/the-uncanny-valley
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective
https://github.com/nutellamok/advrush
Theoretically Principled Trade-off between Robustness and Accuracy
https://github.com/openai/cleverhans
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/optimization-for-data-driven-science/dair
Theoretically Principled Trade-off between Robustness and Accuracy
https://github.com/p2333/score
https://github.com/patrick-mcclure/adversarial_training_for_fmri_decoding
https://github.com/peck94/cann-detector
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/pku-ml/rebat
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
https://github.com/pranjalawasthi/adv_robust_low_rank
https://github.com/ravidziv/dual_IB
https://github.com/retis-ai/patchattacktool
https://github.com/revbucket/mister_ed
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/rezafuru/the-fool
Adversarial Robustness of Bottleneck Injected Deep Neural Networks for Task-Oriented Communication
https://github.com/richterleo/Auditing_Test_for_LMs
https://github.com/roozbeh-yz/similarities
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets
https://github.com/runjiatian/SCI-6338
https://github.com/saintslab/pepr
https://github.com/sayakpaul/Denoised-Smoothing-TF
https://github.com/scenarri/s2m-tea
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/schwinnl/circuit-breakers-eval
https://github.com/sduzpf/UAP_VLP
Universal Adversarial Perturbations for Vision-Language Pre-trained Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.