https://github.com/ehsannowroozi/sec_classifying_url_detection
공식 코드An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework
Papers with Code 아카이브
Adversarial Attack
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/ekko-zn/ijcai2022-backdoor
공식 코드Imperceptible Backdoor Attack: From Input Space to Feature Representation
https://github.com/elcronos/Defense-Friendly
공식 코드Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty
https://github.com/eliot-tron/curvnetattack
공식 코드Adversarial attacks on neural networks through canonical Riemannian foliations
https://github.com/ellezwq/mmcoa
공식 코드Revisiting the Adversarial Robustness of Vision Language Models: a Multimodal Perspective
https://github.com/elliothe/CVPR_2019_PNI
공식 코드https://github.com/enkiwang/Imperceptible-fake-face-antiforensic
공식 코드https://github.com/eric-vader/attack-bo
공식 코드https://github.com/eth-sri/eran
공식 코드https://github.com/ethz-spylab/rlhf-poisoning
공식 코드https://github.com/eurecom-asp/malacopula
공식 코드https://github.com/eurecom-asp/malafide
공식 코드https://github.com/eurecom-fscv/2d-malafide
공식 코드2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems
https://github.com/evaluating-adversarial-robustness/adv-eval-paper
공식 코드https://github.com/event-ahu/adversarial_attack_defense
공식 코드Adversarial Attack for RGB-Event based Visual Object Tracking
https://github.com/event-ahu/openpar
공식 코드Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition
https://github.com/fabiocarrara/cbigan-ad
공식 코드Combining GANs and AutoEncoders for Efficient Anomaly Detection
https://github.com/facebookresearch/OnlineAttacks
공식 코드https://github.com/fangshuman/LLTA
공식 코드https://github.com/fangzhenzhao/anomalydetection-keras
공식 코드A Uniform Framework for Anomaly Detection in Deep Neural Networks
https://github.com/fatemehN/TrackPGD
공식 코드TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers
https://github.com/fduxuan/sdm-attack
공식 코드Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making
https://github.com/flaai/tsfool
공식 코드TSFool: Crafting Highly-Imperceptible Adversarial Time Series through Multi-Objective Attack
https://github.com/fmy266/tpa
공식 코드https://github.com/foreverzyh/defend_framework
공식 코드https://github.com/fra31/fab-attack
공식 코드Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
https://github.com/framartin/lgv-geometric-transferability
공식 코드LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity
https://github.com/framartin/transferable-bnn-adv-ex
공식 코드Efficient and Transferable Adversarial Examples from Bayesian Neural Networks
https://github.com/fredwu-hust/dd-robustbench
공식 코드DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation
https://github.com/ftrojanattack/ftrojan
공식 코드https://github.com/fuhaha824/pg-attack
공식 코드https://github.com/fursovia/dilma
공식 코드Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
https://github.com/gandolfczjh/3d2fool
공식 코드Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving
https://github.com/georgsiedel/adversarial-distance-estimation
공식 코드A practical approach to evaluating the adversarial distance for machine learning classifiers
https://github.com/gmum/mango
공식 코드Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks
https://github.com/godxuxilie/robust-tst
공식 코드Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.