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Adversarial Attack

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전체 기법 전체 데이터셋

하위 태스크: 4
평가표: 3
보존된 평가 기록: 9

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Nanyuu/TUA

공식 코드

A Targeted Universal Attack on Graph Convolutional Network

https://github.com/NiCE-X/TSAA

공식 코드

Transferable Sparse Adversarial Attack

https://github.com/NikolaiKyhne/Detecting-and-Defending-Against-Adversarial-Attacks

공식 코드

Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models

https://github.com/Ninarehm/attack

공식 코드

Exacerbating Algorithmic Bias through Fairness Attacks

https://github.com/OfficialBishal/GeomMaskAttack

공식 코드

Adversarial sample generation and training using geometric masks for accurate and resilient license plate character recognition

https://github.com/PaladinEE15/RSAC

공식 코드

Robust Reinforcement Learning under model misspecification

https://github.com/PanZiqiAI/disentangled-information-bottleneck

공식 코드

Disentangled Information Bottleneck

https://github.com/QData/AdversarialDNN-Playground

공식 코드

Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning

https://github.com/QData/TextAttack

공식 코드

BAE: BERT-based Adversarial Examples for Text Classification

https://github.com/QData/TextAttack-Search-Benchmark

공식 코드

Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples

https://github.com/QData/TextAttack/blob/master/textattack/attack_recipes/bae_garg_2019.py

공식 코드

BAE: BERT-based Adversarial Examples for Text Classification

https://github.com/QData/deepWordBug

공식 코드

Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

https://github.com/QVPR/aarapsiproject

공식 코드

Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot Navigation

https://github.com/RYoungJ/ZO-L2L

공식 코드

Learning to Learn by Zeroth-Order Oracle

https://github.com/RaphaelOlivier/asr_transferability

공식 코드

Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition models

https://github.com/RishabhMaheshwary/hard-label-attack

공식 코드

Generating Natural Language Attacks in a Hard Label Black Box Setting

https://github.com/RjDuan/Advlight

공식 코드

Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink

https://github.com/RussianNLP/TAPE

공식 코드

TAPE: Assessing Few-shot Russian Language Understanding

https://github.com/SafeAILab/RAIN

공식 코드

RAIN: Your Language Models Can Align Themselves without Finetuning

https://github.com/Sandy-Zeng/NPAttack

공식 코드

Improving Query Efficiency of Black-box Adversarial Attack

https://github.com/Sargaleano/Madlib-Spam-Attack-BERT

공식 코드

Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam Detection

https://github.com/SarielMa/ICLR2020_AI4AH

공식 코드

Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective

https://github.com/SewoongLab/FLIP

공식 코드

Label Poisoning is All You Need

https://github.com/SewoongLab/ntk-backdoor

공식 코드

Few-shot Backdoor Attacks via Neural Tangent Kernels

https://github.com/ShannonAI/backdoor_nlg

공식 코드

Defending Against Backdoor Attacks in Natural Language Generation

https://github.com/Shawn-Shan/trapdoor

공식 코드

Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks

https://github.com/ShihaoZhaoZSH/Video-Backdoor-Attack

공식 코드

Clean-Label Backdoor Attacks on Video Recognition Models

https://github.com/Sjtubrian/SAMMD

공식 코드

Maximum Mean Discrepancy Test is Aware of Adversarial Attacks

https://github.com/SongBaiHust/Adversarial_Metric_Attack

공식 코드

Adversarial Metric Attack and Defense for Person Re-identification

https://github.com/Spinozaaa/AdvI2I

공식 코드

AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models

https://github.com/SwiftieH/GFAttack

공식 코드

A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models

https://github.com/THUDM/tdgia

공식 코드

TDGIA:Effective Injection Attacks on Graph Neural Networks

https://github.com/THUYimingLi/backdoor-learning-resources

공식 코드

Backdoor Learning: A Survey

https://github.com/THUrssq/Tianchi04

공식 코드

Sparse Adversarial Attack to Object Detection

https://github.com/TLMichael/Acc-SZOFW

공식 코드

Accelerated Stochastic Gradient-free and Projection-free Methods

https://github.com/Tangyiming205069/controllable-seo

공식 코드

StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization

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