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/Jianbo-Lab/HSJA
공식 코드https://github.com/Jinxhy/AppAIsecurity
공식 코드Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps
https://github.com/JoeyW221/DUAttack
공식 코드https://github.com/JohnsonJiang1996/AdvAttack_LLM4TS
공식 코드Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
https://github.com/Juintin/Bridge-FAST-and-PGD-AT
공식 코드Bridging the Performance Gap between FGSM and PGD Adversarial Training
https://github.com/JunweiLiang/Multiverse/tree/master/SimAug
공식 코드https://github.com/KaidiXu/Beta-CROWN
공식 코드https://github.com/KaidiXu/StrAttack
공식 코드Structured Adversarial Attack: Towards General Implementation and Better Interpretability
https://github.com/KaiyuanZh/FLIP
공식 코드FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
https://github.com/KaosEngineer/PriorNetworks
공식 코드Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
https://github.com/Kim-Minseon/RoCL
공식 코드https://github.com/Kira0096/CGATTACK
공식 코드Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution
https://github.com/KuofengGao/CIBA
공식 코드Backdoor Attack on Hash-based Image Retrieval via Clean-label Data Poisoning
https://github.com/KuofengGao/IRBA
공식 코드https://github.com/KuofengGao/MLLM-Grounding-Robustness
공식 코드Adversarial Robustness for Visual Grounding of Multimodal Large Language Models
https://github.com/LTS4/DeepFool
공식 코드DeepFool: a simple and accurate method to fool deep neural networks
https://github.com/LYZ98/Adversarial-Diffusion-Attacks-on-Graph-based-Traffic-Prediction-Models
공식 코드Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models
https://github.com/LiYingwei/ghost-network
공식 코드Learning Transferable Adversarial Examples via Ghost Networks
https://github.com/LightDXY/GreedyFool
공식 코드https://github.com/LilyNLP/ADFAR
공식 코드https://github.com/LinLabNEU/Blackbox_ADMM
공식 코드https://github.com/LinLabNEU/ZO_NGD_blackbox
공식 코드Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent
https://github.com/LinQinLiang/SSAH-adversarial-attack
공식 코드Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity
https://github.com/LinyangLee/BERT-Attack
공식 코드https://github.com/LotusDYH/ssl_robust
공식 코드https://github.com/Lyz1213/BadEdit
공식 코드https://github.com/MAGICS-LAB/GERM
공식 코드Fast and Low-Cost Genomic Foundation Models via Outlier Removal
https://github.com/MAGICS-LAB/GenoArmory
공식 코드GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models
https://github.com/MTandHJ/roboc
공식 코드An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks
https://github.com/MadryLab/cifar10_challenge
공식 코드Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/MadryLab/mnist_challenge
공식 코드Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Magic-Ma-tech/Audio-Jailbreak-Attacks
공식 코드Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box Framework
https://github.com/MasterBin-IIAU/CSA
공식 코드Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises
https://github.com/Megum1/DFST
공식 코드Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification
https://github.com/MengtingXu1203/EvaluatingRobustness
공식 코드Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.