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Adversarial Attack
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 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/yuehwai0508/Bert_TFL
https://github.com/zetongqi/attacking_neural_networks
https://github.com/zhang-henry/inactive
Towards Imperceptible Backdoor Attack in Self-supervised Learning
https://github.com/zhaoyiran924/probe-sampling
Universal and Transferable Adversarial Attacks on Aligned Language Models
https://github.com/zhipeng-wei/Heuristic_black_box_adversarial_attack_on_video_recognition_models
Heuristic Black-box Adversarial Attacks on Video Recognition Models
https://github.com/zhou0123/JDE-attack
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers
https://github.com/zhou0123/JDE_F_ATTACK
Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers
https://github.com/zibojia/rslad
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/zijianh4/CROP-leaderboard.github.io
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
https://github.com/zjfheart/Friendly-Adversarial-Training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/zlijingtao/Neurobfuscator
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks
https://github.com/zqzqz/AdvTrajectoryPrediction
On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles
https://github.com/zzzace2000/robust_cls_model
https://gitlab.com/secml/secml
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.