https://github.com/winterwindwang/rfla
공식 코드RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World
Papers with Code 아카이브
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/wjjll/target-attack
공식 코드Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit Calibration
https://github.com/wkim97/fsr
공식 코드Feature Separation and Recalibration for Adversarial Robustness
https://github.com/wleiiiii/gym-atc-attack-project
공식 코드Explainable and Safe Reinforcement Learning for Autonomous Air Mobility
https://github.com/wolong3385/EatVul-Resources
공식 코드EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection
https://github.com/wooner49/t-spear-shield
공식 코드https://github.com/wowotou1998/LP-BFGS-attack
공식 코드LP-BFGS attack: An adversarial attack based on the Hessian with limited pixels
https://github.com/wujunjie1998/twga
공식 코드Rethinking Targeted Adversarial Attacks For Neural Machine Translation
https://github.com/xaiveryuan/patchbackdoor
공식 코드PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification
https://github.com/xandery-geek/SAAT
공식 코드Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval
https://github.com/xandery-geek/badcm
공식 코드BadCM: Invisible Backdoor Attack Against Cross-Modal Learning
https://github.com/xiangning-chen/SmoothDARTS
공식 코드Stabilizing Differentiable Architecture Search via Perturbation-based Regularization
https://github.com/xiaofanustc/ME-Sampler
공식 코드Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior
https://github.com/xiaohulugo/ScAR-Scaling-Adversarial-Robustness-for-LiDAR-Object-Detection
공식 코드ScAR: Scaling Adversarial Robustness for LiDAR Object Detection
https://github.com/xiaohulugo/scar-iros2023
공식 코드ScAR: Scaling Adversarial Robustness for LiDAR Object Detection
https://github.com/xiaosen-wang/SEM
공식 코드Natural Language Adversarial Defense through Synonym Encoding
https://github.com/xiaosen-wang/TA
공식 코드Triangle Attack: A Query-efficient Decision-based Adversarial Attack
https://github.com/xiaosen-wang/sit
공식 코드Structure Invariant Transformation for better Adversarial Transferability
https://github.com/xiaoxuannlp/ct-gat
공식 코드CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability
https://github.com/xiasong0501/grat
공식 코드Transferable Adversarial Attacks on SAM and Its Downstream Models
https://github.com/xingyizhao/tampers
공식 코드https://github.com/xpf/Multi-Level-MMD-Regularization
공식 코드Enhancing Backdoor Attacks with Multi-Level MMD Regularization
https://github.com/xtLyu/CrossBA
공식 코드Cross-Context Backdoor Attacks against Graph Prompt Learning
https://github.com/xuanqing94/AdvGAN
공식 코드https://github.com/xuanqing94/RobGAN
공식 코드https://github.com/xuefusiji/badrefsr
공식 코드BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution
https://github.com/xunguangwang/cgat
공식 코드CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval
https://github.com/xunguangwang/instructta
공식 코드InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models
https://github.com/xuwkk/aaa_load_forecast
공식 코드Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting
https://github.com/xyzacademic/mlp01example
공식 코드https://github.com/yaliMa/Adversarial-Patch-3D
공식 코드Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes
https://github.com/yangarbiter/adversarial-nonparametrics
공식 코드Robustness for Non-Parametric Classification: A Generic Attack and Defense
https://github.com/yangli-lab/artifact-eraser
공식 코드Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
https://github.com/yangyi-chen/maya
공식 코드Multi-granularity Textual Adversarial Attack with Behavior Cloning
https://github.com/yaodongyu/TRADES
공식 코드Theoretically Principled Trade-off between Robustness and Accuracy
https://github.com/yaoli90/at-pinn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.