https://github.com/dyoony/arow
공식 코드Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples
Papers with Code 아카이브
Adversarial Robustness
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 33
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/dyoony/srst_awr
공식 코드https://github.com/ecrows/cgtext-detection-adv
공식 코드Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers
https://github.com/eheinert/reducing-texture-bias-of-dnns-via-eed
공식 코드Reducing Texture Bias of Deep Neural Networks via Edge Enhancing Diffusion
https://github.com/ehw-fit/rohnas
공식 코드https://github.com/ehyaei/fair-robust-algorithmic-resource
공식 코드https://github.com/ehyaei/robustness-implies-fairness
공식 코드https://github.com/elieattias1/pixel-reg
공식 코드https://github.com/ellezwq/mmcoa
공식 코드Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective
https://github.com/emaballarin/CARSO
공식 코드https://github.com/emanuelelm/the-king-is-naked
공식 코드The King is Naked: on the Notion of Robustness for Natural Language Processing
https://github.com/eminorhan/resnext-wsl
공식 코드https://github.com/ericyinyzy/vlattack
공식 코드VLATTACK: Multimodal Adversarial Attacks on Vision-Language Tasks via Pre-trained Models
https://github.com/eth-sri/sabr
공식 코드https://github.com/ethancaballero/broken_neural_scaling_laws
공식 코드https://github.com/ethos-lab/robust-representation-matching
공식 코드Transferring Adversarial Robustness Through Robust Representation Matching
https://github.com/ethz-privsec/diffusion_denoised_smoothing
공식 코드https://github.com/evaluating-adversarial-robustness/adv-eval-paper
공식 코드https://github.com/explain3d/exp-one-point-atk-pc
공식 코드Explainability-Aware One Point Attack for Point Cloud Neural Networks
https://github.com/facebookresearch/AugLy
공식 코드https://github.com/facebookresearch/ImageNet-Adversarial-Training
공식 코드https://github.com/fairdeface/FairDeface
공식 코드FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods
https://github.com/falesiani/gib
공식 코드Gated Information Bottleneck for Generalization in Sequential Environments
https://github.com/fanghenshaometeor/GenerativeRFF
공식 코드End-to-end Kernel Learning via Generative Random Fourier Features
https://github.com/fanghenshaometeor/Orthogonal-Multi-Path
공식 코드https://github.com/fanghenshaometeor/diversity-via-orthogonality
공식 코드https://github.com/fartashf/robust_bias
공식 코드Bridging the Gap Between Adversarial Robustness and Optimization Bias
https://github.com/fartashf/vsepp
공식 코드https://github.com/fat-cs/convergesmooth
공식 코드https://github.com/fatemehN/TrackPGD
공식 코드TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers
https://github.com/fatemehn/reproducibilitystudy
공식 코드Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers
https://github.com/ffhibnese/model-inversion-attack-toolbox
공식 코드MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense
https://github.com/floriankrach/regretoptimalfederatedtransferlearning
공식 코드https://github.com/fra31/auto-attack
공식 코드Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
https://github.com/fra31/evaluating-adaptive-test-time-defenses
공식 코드Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
https://github.com/fra31/robust-finetuning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.