Adversarial Attack

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 601–625 of 1808 papers

Title	Date	Tasks	Status	Score
Geometry-Aware Generation of Adversarial Point Clouds	Dec 24, 2019	Adversarial AttackFairness	CodeCode Available	5
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks	Apr 18, 2019	Adversarial AttackAdversarial Attack Detection	CodeCode Available	5
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality Metrics	Apr 11, 2023	Adversarial AttackNo-Reference Image Quality Assessment	CodeCode Available	5
DAmageNet: A Universal Adversarial Dataset	Dec 16, 2019	Adversarial Attack	CodeCode Available	5
Graph Neural Network Explanations are Fragile	Jun 5, 2024	Adversarial AttackGraph Neural Network	CodeCode Available	5
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts	Dec 1, 2021	Adversarial Attacktext-classification	CodeCode Available	5
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions	Apr 14, 2020	Adversarial AttackEmotion Classification	CodeCode Available	5
Accuracy of TextFooler black box adversarial attacks on 01 loss sign activation neural network ensemble	Feb 12, 2024	Adversarial AttackClassification	CodeCode Available	5
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available	5
Delving into Transferable Adversarial Examples and Black-box Attacks	Nov 8, 2016	Adversarial AttackAdversarial Defense	CodeCode Available	5
Fast Inference of Removal-Based Node Influence	Mar 13, 2024	Adversarial Attackcounterfactual	CodeCode Available	5
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness	Nov 10, 2023	Adversarial AttackDiversity	CodeCode Available	5
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	5
Curls & Whey: Boosting Black-Box Adversarial Attacks	Apr 2, 2019	Adversarial AttackDiversity	CodeCode Available	5
Explainable Graph Neural Networks Under Fire	Jun 10, 2024	Adversarial Attack	CodeCode Available	5
Adversarial and Clean Data Are Not Twins	Apr 17, 2017	Adversarial Attack	CodeCode Available	5
Detecting Adversarial Perturbations in Multi-Task Perception	Mar 2, 2022	Adversarial AttackDepth Estimation	CodeCode Available	5
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models	Sep 12, 2024	Adversarial AttackAdversarial Purification	CodeCode Available	5
CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability	Oct 22, 2023	Adversarial Attack	CodeCode Available	5
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial Attacks	Nov 3, 2020	Adversarial AttackMachine Translation	CodeCode Available	5
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks	Jul 1, 2020	Adversarial Attack	CodeCode Available	5
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective	Jan 1, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	5
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks	Jul 12, 2021	Adversarial Attack	CodeCode Available	5

Show:10 25 50

← PrevPage 25 of 73Next →

All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified