Papers with Code 아카이브
Adversarial Attack
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 3
- 보존된 평가 기록
- 9
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/elites2k19/prism-attack
https://github.com/epfl-ml-reproducers/subspace-attack-reproduction
Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
https://github.com/erbloo/dr
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction
https://github.com/ericlee8/space
https://github.com/ermongroup/generative_adversary
Provable defenses against adversarial examples via the convex outer adversarial polytope
https://github.com/ethz-spylab/rlhf_trojan_competition
https://github.com/faizaan09/caption-to-image-generation
https://github.com/formal-verification-research/NJSMA
https://github.com/fra31/mmr-universal
Provable defenses against adversarial examples via the convex outer adversarial polytope
https://github.com/fra31/rlhf-trojan-competition-submission
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/framartin/trap
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/franroldans/tfm-franroldan-wav2pix
https://github.com/frgfm/torch-cam
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
https://github.com/fsept11/FoolHD
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
https://github.com/fsepteixeira/FoolHD
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
https://github.com/giantseaweed/decree
BadEncoder: Backdoor Attacks to Pre-trained Encoders in Self-Supervised Learning
https://github.com/goldblum/AdversariallyRobustDistillation
Theoretically Principled Trade-off between Robustness and Accuracy
https://github.com/grayswanai/nanogcg
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/halimiqi/CD-ATTACK
Adversarial Attack on Community Detection by Hiding Individuals
https://github.com/hanzhanggit/StackGAN
https://github.com/hanzhanggit/StackGAN-Pytorch
https://github.com/hanzhanggit/StackGAN-inception-model
https://github.com/hanzhanggit/StackGAN-v2
https://github.com/henry8527/GCE
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/hope-yao/robust_attention_cifar
https://github.com/hrdwsong/TDLMR2AA-Paddle
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/huanzhang12/CROWN-Robustness-Certification
Provable defenses against adversarial examples via the convex outer adversarial polytope
https://github.com/huanzhang12/alpha-beta-CROWN
https://github.com/huanzhang12/sa_ppo
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
https://github.com/huawei-noah/kd-nlp
MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation
https://github.com/hugolan/Project-Bachelor
https://github.com/huisun317/snn
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
https://github.com/huitailangyz/MGAA
https://github.com/idt-iti/xai-deepfakes
An adversarial attack approach for eXplainable AI evaluation on deepfake detection models
https://github.com/igitugraz/outputcodematching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.