https://github.com/revbucket/mister_ed
Towards Deep Learning Models Resistant to Adversarial Attacks
Papers with Code 아카이브
Adversarial Attack
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.
Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rightlit/StackGAN-v2-rev
https://github.com/rotaryhammer/code-autodan
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/rtoth11/NNAdversary-MIPVerify
Provable defenses against adversarial examples via the convex outer adversarial polytope
https://github.com/ruoxi-jia-group/narcissus
Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information
https://github.com/salomonhotegni/MOREL
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/sarielma/robust_dnn_for_ecg
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective
https://github.com/scenarri/s2m-tea
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/scrambleegg7/Text-to-Image-Synthesis2
https://github.com/sduzpf/UAP_VLP
Universal Adversarial Perturbations for Vision-Language Pre-trained Models
https://github.com/serval-uni-lu/tabularbench
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space
https://github.com/shaguopohuaizhe/TSAA
https://github.com/shijiawenwen/judgedeceiver
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/shijiel2/cleverhans
https://github.com/shijing001/VIB_audio_classification
https://github.com/shubhamuttam1/Pytorch-DAA
https://github.com/shuchiwu/rda
BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning
https://github.com/sigmaister/disrupting-deepfakes-_-
https://github.com/simon0987/Fast_FGSM
https://github.com/sipka/fw-adversarial-imagenet
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
https://github.com/snow-mn/GAN-INT-CLS
https://github.com/srayhit/TextToImageSynthesisUsingGAN
https://github.com/srk97/targeted-adversarial-mnist
https://github.com/szegedai/robust-segmentation-evaluation
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack
https://github.com/tabayashi0117/Score-CAM
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
https://github.com/taoshuchang/g-nia
Adversarial Immunization for Certifiable Robustness on Graphs
https://github.com/taranO/defending-adversarial-attacks-by-RD
https://github.com/tataiani/enhanced_regularizers_attributional_robustness
https://github.com/tcwangshiqi-columbia/Neurify
https://github.com/tcwangshiqi-columbia/ReluVal
https://github.com/textflint/textflint
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
https://github.com/thomashopkins32/PGDAdversarialLearning
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/thompsonj/nanoGCG
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/thu-coai/safeunlearning
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/thunlp/DictSKB
Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Trigger
https://github.com/timroith/adversarialcbo
Square Attack: a query-efficient black-box adversarial attack via random search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.