Papers with Code 아카이브
Adversarial Defense
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 10
- 보존된 평가 기록
- 28
Competitions with currently unpublished results:
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/huanzhang12/ZOO-Attack
공식 코드https://github.com/hubarruby/maskpure
공식 코드MaskPure: Improving Defense Against Text Adversaries with Stochastic Purification
https://github.com/iVMCL/AOGNets
공식 코드AOGNets: Compositional Grammatical Architectures for Deep Learning
https://github.com/inspire-group/privacy-vs-robustness
공식 코드Privacy Risks of Securing Machine Learning Models against Adversarial Examples
https://github.com/itailang/geometric_adv
공식 코드Geometric Adversarial Attacks and Defenses on 3D Point Clouds
https://github.com/javyduck/diffsmooth
공식 코드DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing
https://github.com/jiequancui/DKL
공식 코드https://github.com/jiequancui/LBGAT
공식 코드https://github.com/jinyiw/guideddiffusionpur
공식 코드https://github.com/jmyoon1/adp
공식 코드https://github.com/joana-cabral/lisard
공식 코드LISArD: Learning Image Similarity to Defend Against Gray-box Adversarial Attacks
https://github.com/joellliu/segmentandcomplete
공식 코드https://github.com/john-niu-07/pud
공식 코드Towards Unified Robustness Against Both Backdoor and Adversarial Attacks
https://github.com/jokeryan/post_training
공식 코드https://github.com/junzhuang-code/lindt
공식 코드https://github.com/kabkabm/defensegan
공식 코드Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
https://github.com/kaidi-jin/backdoor_samples_detection
공식 코드Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?
https://github.com/kaleab-k/VideoAT
공식 코드Analysis and Extensions of Adversarial Training for Video Classification
https://github.com/kangmintong/diffattack
공식 코드DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
https://github.com/khalooei/lsa
공식 코드Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework
https://github.com/kimianoorbakhsh/lpf-defence
공식 코드LPF-Defense: 3D Adversarial Defense based on Frequency Analysis
https://github.com/kimianoorbakhsh/lpf-defense
공식 코드LPF-Defense: 3D Adversarial Defense based on Frequency Analysis
https://github.com/kkkcx/diffender
공식 코드DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
https://github.com/konpanousis/adversarial-lwta-autoattack
공식 코드Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
https://github.com/lfz/Guided-Denoise
공식 코드Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
https://github.com/lgm70/smug
공식 코드SMUG: Towards robust MRI reconstruction by smoothed unrolling
https://github.com/liaopeiyuan/GAL
공식 코드https://github.com/lixiaothu/laat
공식 코드Language-Driven Anchors for Zero-Shot Adversarial Robustness
https://github.com/locuslab/projected_sinkhorn
공식 코드Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
https://github.com/locuslab/smoothing
공식 코드https://github.com/mahyarnajibi/FreeAdversarialTraining
공식 코드https://github.com/mamerzouk/adversarial-purification
공식 코드Diffusion-based Adversarial Purification for Intrusion Detection
https://github.com/mehdijj/ppd
공식 코드PPD: Permutation Phase Defense Against Adversarial Examples in Deep Learning
https://github.com/michaeltian108/ca-tride
공식 코드Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
https://github.com/minicheshire/deq-white-box-robustness
공식 코드A Closer Look at the Adversarial Robustness of Deep Equilibrium Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.