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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/anishathalye/obfuscated-gradients

공식 코드

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

https://github.com/anjilab/secure-private-ai

공식 코드

Adversarial Robustness Analysis of Vision-Language Models in Medical Image Segmentation

https://github.com/annedirkson/breakingbert

공식 코드

Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial Attack

https://github.com/anonymous0120/dr

공식 코드

Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction

https://github.com/anonymoususera/ava

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AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection

https://github.com/anshuman23/cfc

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Robust Fair Clustering: A Novel Fairness Attack and Defense Framework

https://github.com/anubhav1997/watermark_forgery_removal

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Forging and Removing Latent-Noise Diffusion Watermarks Using a Single Image

https://github.com/aounon/certified-llm-safety

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Certifying LLM Safety against Adversarial Prompting

https://github.com/apple/axlearn/blob/main/axlearn/vision/imagenet_adversarial_text/

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Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness

https://github.com/arcelikacikkaynak/federated_learning

공식 코드

Anomaly Localization in Model Gradients Under Backdoor Attacks Against Federated Learning

https://github.com/aries-iai/tt3d

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Towards Transferable Targeted 3D Adversarial Attack in the Physical World

https://github.com/aryan-satpathy/backdoor

공식 코드

Towards Adversarial Robustness And Backdoor Mitigation in SSL

https://github.com/ase2023paper/fmt

공식 코드

Adversarial Feature Map Pruning for Backdoor

https://github.com/ashafahi/free_adv_train

공식 코드

Adversarial Training for Free!

https://github.com/asif-hanif/baple

공식 코드

BAPLe: Backdoor Attacks on Medical Foundational Models using Prompt Learning

https://github.com/asif-hanif/vafa

공식 코드

Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation

https://github.com/askexplain/sampling4sharing

공식 코드

Generate synthetic samples from tabular data

https://github.com/awslabs/gluonts

공식 코드

Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms

https://github.com/azrealwang/mtadv

공식 코드

A Multi-task Adversarial Attack Against Face Authentication

https://github.com/azrealwang/sgadv

공식 코드

Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

https://github.com/bapfl/code

공식 코드

You Can Backdoor Personalized Federated Learning

https://github.com/batuceng/e-mesh-attack

공식 코드

epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition

https://github.com/bboylyg/abl

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Anti-Backdoor Learning: Training Clean Models on Poisoned Data

https://github.com/benfinkelshtein/SINGLE

공식 코드

Single-Node Attacks for Fooling Graph Neural Networks

https://github.com/bethgelab/foolbox

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Foolbox: A Python toolbox to benchmark the robustness of machine learning models

https://github.com/bhheo/BSS_distillation

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Knowledge Distillation with Adversarial Samples Supporting Decision Boundary

https://github.com/bigml-cs-ucla/roclip

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Robust Contrastive Language-Image Pre-training against Data Poisoning and Backdoor Attacks

https://github.com/bingyaohuang/spaa

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SPAA: Stealthy Projector-based Adversarial Attacks on Deep Image Classifiers

https://github.com/bjbbbb/enhancing-adversarial-robustness-with-conformal-prediction

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Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability

https://github.com/brittany-chen/svastin

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SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks

https://github.com/c0510gy/AFLS-AED

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A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection

https://github.com/caoyu-noob/tasa

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TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack

https://github.com/captaincuong/robusttext_acl2024

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A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models

https://github.com/caradryanliang/improvedadvdm

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Targeted Attack Improves Protection against Unauthorized Diffusion Customization

https://github.com/carlini/AmI

공식 코드

Is AmI (Attacks Meet Interpretability) Robust to Adversarial Examples?

https://github.com/cas-lrj/deeppac

공식 코드

Towards Practical Robustness Analysis for DNNs based on PAC-Model Learning

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