https://github.com/shaoyuanlo/artuda
공식 코드Exploring Adversarially Robust Training for Unsupervised Domain Adaptation
Papers with Code 아카이브
Adversarial Robustness
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shaoyuanlo/principals
공식 코드https://github.com/shashankkotyan/DualQualityAssessment
공식 코드Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary
https://github.com/shashankskagnihotri/adv-corrected-ddcat-cospgd
공식 코드CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
https://github.com/shashankskagnihotri/cospgd
공식 코드CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
https://github.com/shehzaadzd/variational-classification
공식 코드https://github.com/shikiw/robustmae
공식 코드Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting
https://github.com/shiyuchengtju/par
공식 코드Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal
https://github.com/shizhouxing/Attack-LM-Detectors
공식 코드https://github.com/shoumiksaha/drsm
공식 코드DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
https://github.com/shubhamagarwal12/warlearn
공식 코드https://github.com/shupenggui/ATMC
공식 코드Model Compression with Adversarial Robustness: A Unified Optimization Framework
https://github.com/shwinshaker/RobustDataProfiling
공식 코드Data Quality Matters For Adversarial Training: An Empirical Study
https://github.com/sigma0-advx/sigma-zero
공식 코드$σ$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
https://github.com/simonucl/adv-retreival-icl
공식 코드Evaluating and Safeguarding the Adversarial Robustness of Retrieval-Based In-Context Learning
https://github.com/sinabaharlouei/multipleabstaindetection
공식 코드Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes
https://github.com/singlasahil14/SOC
공식 코드https://github.com/singlasahil14/improved_l2_robustness
공식 코드https://github.com/sjladams/bnn_dp
공식 코드BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
https://github.com/sjtu-xai-lab/transformation-complexity
공식 코드Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs
https://github.com/sjtubrian/mm-attack
공식 코드Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
https://github.com/sjyucnel/cauchy-schwarz-information-bottleneck
공식 코드Cauchy-Schwarz Divergence Information Bottleneck for Regression
https://github.com/smavali/aigi-break
공식 코드Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors
https://github.com/snappymanatee/robust-learned-representations
공식 코드Adversarial Robustness as a Prior for Learned Representations
https://github.com/sndnyang/GMMC
공식 코드Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More
https://github.com/sndnyang/sadajem
공식 코드Towards Bridging the Performance Gaps of Joint Energy-based Models
https://github.com/snu-mllab/PuzzleMix
공식 코드Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
https://github.com/softsys4ai/cf-amc-ssl
공식 코드https://github.com/softsys4ai/cl-robustness
공식 코드Rethinking Robust Contrastive Learning from the Adversarial Perspective
https://github.com/sohaib730/argos-adversarial_detection
공식 코드https://github.com/sophiaas/bispectral-networks
공식 코드https://github.com/sowgali/get-fooled-for-the-right-reason
공식 코드https://github.com/spfrommer/projected_randomized_smoothing
공식 코드Projected Randomized Smoothing for Certified Adversarial Robustness
https://github.com/spring-epfl/transferable-cat-robustness
공식 코드Transferable Adversarial Robustness for Categorical Data via Universal Robust Embeddings
https://github.com/spring-epfl/trickster
공식 코드Evading classifiers in discrete domains with provable optimality guarantees
https://github.com/ssharmin/spikingNN-adversarial-attack
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.