https://github.com/guanghelee/Randomized_Smoothing
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
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/hamidmousavi0/ProARD
ProARD: progressive adversarial robustness distillation: provide wide range of robust students
https://github.com/hding2455/carts
https://github.com/hed-ucas/alphadecay
https://github.com/henry8527/GCE
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/hirokiadachi/Adversarial-vertex-mixup-pytorch
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
https://github.com/hope-yao/robust_attention_cifar
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/hrdwsong/TDLMR2AA-Paddle
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/huanranchen/AdversarialAttacks/tree/main/experiments/DiffusionClassifier
https://github.com/huanzhang12/alpha-beta-CROWN
https://github.com/huawei-noah/kd-nlp
https://github.com/imrahulr/adversarial_robustness_pytorch
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/imrahulr/hat
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
https://github.com/j-cb/GOOD
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
https://github.com/jayjaynandy/RBF_CNN-v2
Approximate Manifold Defense Against Multiple Adversarial Perturbations
https://github.com/jcruan519/petrobustness
Understanding Robustness of Visual State Space Models for Image Classification
https://github.com/jerermyyoung/rtlearning
https://github.com/jiaxiaojunQAQ/FGSM-LAW
https://github.com/jiequancui/LBGAT
https://github.com/joakimedin/medical-coding-reproducibility
An Unsupervised Approach to Achieve Supervised-Level Explainability in Healthcare Records
https://github.com/joe1chief/windownormalizaion
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
https://github.com/jokeryan/post_training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/jonasrauber/analysis-by-synthesis
Towards the first adversarially robust neural network model on MNIST
https://github.com/jsrdcht/SSL-Backdoor
An Embarrassingly Simple Backdoor Attack on Self-supervised Learning
https://github.com/khieu/cifar10_challenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/kschwethelm/hyperboliccv
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers
https://github.com/lemonadec/Relevance-between-Accuracy-under-Black-box-Attack-and-the-Similarity-between-Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/leondiao0427/seas
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming
https://github.com/liangbright/OOD_Attack_NN
https://github.com/liangxiaoxuan/Image-processing-DL
Improving Adversarial Robustness via Promoting Ensemble Diversity
https://github.com/lirundong/quant-pack
https://github.com/llylly/dsrs
https://github.com/locuslab/convex_adversarial
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/locuslab/fast_adversarial
https://github.com/locuslab/robust_overfitting
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/loes5307/vocaladversary2022
Towards Deep Learning Models Resistant to Adversarial Attacks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.