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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/retis-ai/carla-gear

공식 코드

CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models

https://github.com/retis-ai/semsegadvpatch

공식 코드

On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving

https://github.com/rgerum/spectral-representations

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Different Spectral Representations in Optimized Artificial Neural Networks and Brains

https://github.com/ricardodominguez/adversariallyrobustrecourse

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On the Adversarial Robustness of Causal Algorithmic Recourse

https://github.com/rice-eic/robust-scratch-ticket

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Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks

https://github.com/rjduan/advdrop

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AdvDrop: Adversarial Attack to DNNs by Dropping Information

https://github.com/robinther/dense_associative_network

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Dense Hopfield Networks in the Teacher-Student Setting

https://github.com/rohban-lab/Salehi_submitted_2020

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ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection

https://github.com/rohban-lab/Shaeiri_submitted_2020

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Towards Deep Learning Models Resistant to Large Perturbations

https://github.com/rohban-lab/patchgaurd

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PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies

https://github.com/rohban-lab/patchguard

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PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies

https://github.com/roinaveiro/aml_bayes

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Adversarial Machine Learning: Bayesian Perspectives

https://github.com/royg27/pag-rob

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Do Perceptually Aligned Gradients Imply Adversarial Robustness?

https://github.com/roywang021/UMK

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White-box Multimodal Jailbreaks Against Large Vision-Language Models

https://github.com/rpnode-fss/rpnode_fss

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Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs

https://github.com/ruizheng20/robust_ticket

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Robust Lottery Tickets for Pre-trained Language Models

https://github.com/ruizhong961230/llmo

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Large Language Model Assisted Adversarial Robustness Neural Architecture Search

https://github.com/ryancampbell514/HeatSmoothing

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Deterministic Gaussian Averaged Neural Networks

https://github.com/s1m0n38/semantic-encodings

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Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers

https://github.com/saehyung-lee/biamat

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Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training

https://github.com/sagarverma/transformer-lipschitz

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CertViT: Certified Robustness of Pre-Trained Vision Transformers

https://github.com/saintslab/adver-fine

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Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency

https://github.com/saintslab/pytoch

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Is Adversarial Training with Compressed Datasets Effective?

https://github.com/salomonhotegni/MOREL

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MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning

https://github.com/samuelemarro/counter-attack

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Computational Asymmetries in Robust Classification

https://github.com/sarielma/robust_dnn_for_ecg

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A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification

https://github.com/scenarri/SMGAA

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Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense

https://github.com/schwinnl/llm_embedding_attack

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Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space

https://github.com/schzhu/learning-adversarially-robust-representations

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Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization

https://github.com/sennadir/gcorn

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Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks

https://github.com/serendipity1122/pre-trained-model-guided-fine-tuning-for-zero-shot-adversarial-robustness

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Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness

https://github.com/serval-uni-lu/realistic_adversarial_hardening

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On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks

https://github.com/serval-uni-lu/tabularbench

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TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases

https://github.com/shahrukhx01/adversarial-bert-german-attacks-defense

공식 코드

White-Box Attacks on Hate-speech BERT Classifiers in German with Explicit and Implicit Character Level Defense

https://github.com/shaoyuanlo/Halftoning-Defense

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Error Diffusion Halftoning Against Adversarial Examples

https://github.com/shaoyuanlo/OUDefend

공식 코드

Overcomplete Representations Against Adversarial Videos

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