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Adversarial Robustness

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하위 태스크: 0
평가표: 7
보존된 평가 기록: 33

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/sforsyth6/transfer_adversarial

Adversarial Robustness Toolbox v1.0.0

https://github.com/sgxcj777/Adversarial-testing-toolbox-with-CLEVER

Adversarial Robustness Toolbox v1.0.0

https://github.com/sgxcj777/Advesarial-robustess-evaluation-Framework

Adversarial Robustness Toolbox v1.0.0

https://github.com/shivamsaboo17/ManifoldMixup

Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy

https://github.com/shizhouxing/LLM-Detector-Robustness

Red Teaming Language Model Detectors with Language Models

https://github.com/simon0987/Fast_FGSM

On Evaluating Adversarial Robustness

https://github.com/simplymathematics/deckard/tree/main/examples/pytorch

A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (TRASHFIRE)

https://github.com/ssysarch/LightPure

LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models

https://github.com/tensorflow/cleverhans

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/testingautomated-usi/fashion-mnist-c

MNIST-C: A Robustness Benchmark for Computer Vision

https://github.com/thomashopkins32/PGDAdversarialLearning

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/thu-ml/realsafe

RobFR: Benchmarking Adversarial Robustness on Face Recognition

https://github.com/tuananhbui89/ASCL

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

https://github.com/umd-huang-lab/vlm-poisoning

Exploring and Exploiting Decision Boundary Dynamics for Adversarial Robustness

https://github.com/ustunb/reachml

Prediction without Preclusion: Recourse Verification with Reachable Sets

https://github.com/verified-intelligence/alpha-beta-crown

Provably Bounding Neural Network Preimages

https://github.com/vita-epfl/s-attack

Are socially-aware trajectory prediction models really socially-aware?

https://github.com/vita-epfl/semdisc

Are socially-aware trajectory prediction models really socially-aware?

https://github.com/vita-group/atmc

Model Compression with Adversarial Robustness: A Unified Optimization Framework

https://github.com/wzekai99/dm-improves-at

Fixing Data Augmentation to Improve Adversarial Robustness

https://github.com/xieyong0/PMA

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks

https://github.com/xzh0u/randomized-smoothing

Certified Adversarial Robustness with Additive Noise

https://github.com/yascho/interception_smoothing

Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks

https://github.com/yguooo/semisup-adv

Unlabeled Data Improves Adversarial Robustness

https://github.com/yizhe-ang/fake-detection-lab

Adversarial Attack on Deep Learning-Based Splice Localization

https://github.com/yqtianust/Awesome-SE-for-AI

APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection

https://github.com/zhaohuajing/CaptchaGAN

Generating Adversarial Examples with Adversarial Networks

https://github.com/zijianh4/CROP-leaderboard.github.io

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/zjfheart/Friendly-Adversarial-Training

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/zjysteven/DVERGE

Improving Adversarial Robustness via Promoting Ensemble Diversity

https://github.com/zqzqz/AdvTrajectoryPrediction

On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles

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