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/usail-hkust/bkdfedgcn
공식 코드Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network
https://github.com/usc-sail/gard-adversarial-speaker-id
공식 코드Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems
https://github.com/utibetnlp/tsattacker
공식 코드https://github.com/utsjiyaoli/adversarial-image-captioning-attack
공식 코드AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization
https://github.com/vadel/acpd
공식 코드Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
https://github.com/val-iisc/GAMA-GAT
공식 코드Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
https://github.com/val-iisc/gd-uap
공식 코드Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations
https://github.com/vanpe20/BATprompt
공식 코드https://github.com/vdigpku/cmua-watermark
공식 코드CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
https://github.com/vdigpku/t-sea
공식 코드T-SEA: Transfer-based Self-Ensemble Attack on Object Detection
https://github.com/venomenhancer/venom
공식 코드A general approach to enhance the survivability of backdoor attacks by decision path coupling
https://github.com/ventr1c/ugba
공식 코드https://github.com/vinusankars/beast
공식 코드Fast Adversarial Attacks on Language Models In One GPU Minute
https://github.com/virresh/viking
공식 코드Adversarial Attack on Network Embeddings via Supervised Network Poisoning
https://github.com/vision4robotics/ad2attack
공식 코드Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking
https://github.com/vl-group/gaker
공식 코드Any Target Can be Offense: Adversarial Example Generation via Generalized Latent Infection
https://github.com/vtddggg/CAA
공식 코드https://github.com/walegahaha/Curls-Whey
공식 코드https://github.com/wang-axis/dna
공식 코드Disrupting Adversarial Transferability in Deep Neural Networks
https://github.com/wang-axis/dna_ecg
공식 코드Decorrelative Network Architecture for Robust Electrocardiogram Classification
https://github.com/wangfrombupt/adversarial_detector
공식 코드https://github.com/wanghangpsu/mm-bd
공식 코드https://github.com/wangjksjtu/minmax-adv
공식 코드Adversarial Attack Generation Empowered by Min-Max Optimization
https://github.com/wangren09/MetaAdv
공식 코드On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
https://github.com/wangren09/TrojanNetDetector
공식 코드Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
https://github.com/wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List
공식 코드Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition
https://github.com/warisgill/FedDefender
공식 코드https://github.com/wecanshow/mcl
공식 코드https://github.com/weihui1308/hotcoldblock
공식 코드HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design
https://github.com/weihui1308/paa
공식 코드Physical Adversarial Attack meets Computer Vision: A Decade Survey
https://github.com/weijiawu/SyntoReal_STD
공식 코드Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild
https://github.com/weikai-li/nora
공식 코드https://github.com/whj363636/Adversarial-attack-on-Person-ReID-With-Deep-Mis-Ranking
공식 코드https://github.com/wickstrom/quantitative-xai-manipulation
공식 코드From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation
https://github.com/windvchen/diffattack
공식 코드Diffusion Models for Imperceptible and Transferable Adversarial Attack
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.