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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/jiasongchen/SPIE-2021
공식 코드https://github.com/jiaweilian/benchmarking_physical_attack
공식 코드PADetBench: Towards Benchmarking Physical Attacks against Object Detection
https://github.com/jiaweilian/cba
공식 코드CBA: Contextual Background Attack against Optical Aerial Detection in the Physical World
https://github.com/jiaweilian/padetbench
공식 코드PADetBench: Towards Benchmarking Physical Attacks against Object Detection
https://github.com/jie-ren/a-unified-game-theoretic-interpretation-of-adversarial-robustness
공식 코드A Unified Game-Theoretic Interpretation of Adversarial Robustness
https://github.com/jiequancui/DKL
공식 코드https://github.com/jlwu002/bcl
공식 코드Robust Deep Reinforcement Learning through Bootstrapped Opportunistic Curriculum
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/joksas/nonideality-aware-memristive-networks-under-attack
공식 코드Nonideality-aware training makes memristive networks more robust to adversarial attacks
https://github.com/jonahobw/aicas
공식 코드Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning
https://github.com/jonas-maj/ensemble-adversarial-robustness
공식 코드https://github.com/jordantab/llm-robustness-experiment
공식 코드On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models
https://github.com/josuenassar/power_law
공식 코드https://github.com/juancprzs/TTE
공식 코드Enhancing Adversarial Robustness via Test-time Transformation Ensembling
https://github.com/kaihuatang/ciiv-adversarial-robustness.pytorch
공식 코드https://github.com/kaleab-k/VideoAT
공식 코드Analysis and Extensions of Adversarial Training for Video Classification
https://github.com/kastel-mobilitylab/mixtures-of-experts
공식 코드https://github.com/kaustubhsridhar/PoE-robustness
공식 코드Improving Neural Network Robustness via Persistency of Excitation
https://github.com/kenny-co/sgd-uap-torch
공식 코드Universal Adversarial Robustness of Texture and Shape-Biased Models
https://github.com/kevivk/mwp_adversarial
공식 코드Adversarial Examples for Evaluating Math Word Problem Solvers
https://github.com/kimvc7/HDL
공식 코드https://github.com/kinit-sk/mAO
공식 코드Authorship Obfuscation in Multilingual Machine-Generated Text Detection
https://github.com/kiode/text_watermark_language_models
공식 코드https://github.com/koava36/certrob-fewshot
공식 코드https://github.com/konpanousis/adversarial-lwta-autoattack
공식 코드Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
https://github.com/kothasuhas/verify-input
공식 코드https://github.com/ksandeshk/spatial-vs-robustness
공식 코드Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks
https://github.com/kugwzk/at_diff
공식 코드Improved Diffusion-based Generative Model with Better Adversarial Robustness
https://github.com/kvgandikota/robustness-low-dose-ct
공식 코드https://github.com/kylematoba/lcnn
공식 코드https://github.com/kyungmi-lee/eval-adv-robustness
공식 코드Rethinking Empirical Evaluation of Adversarial Robustness Using First-Order Attack Methods
https://github.com/laion-ai/clip_benchmark
공식 코드LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders
https://github.com/layer6ai-labs/ssl-robustness
공식 코드Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
https://github.com/lazizcodes/twicing_attention
공식 코드Transformer Meets Twicing: Harnessing Unattended Residual Information
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