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 576–600 of 1808 papers

Title	Date	Tasks	Status	Score
Fashion-Guided Adversarial Attack on Person Segmentation	Apr 17, 2021	Adversarial AttackHuman Instance Segmentation	CodeCode Available	5
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient Estimation	Mar 9, 2023	Adversarial Attack	CodeCode Available	5
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions	Apr 14, 2020	Adversarial AttackEmotion Classification	CodeCode Available	5
Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA	Nov 5, 2020	Adversarial AttackCloud Computing	CodeCode Available	5
Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning	Oct 31, 2023	Adversarial Attackreinforcement-learning	CodeCode Available	5
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation	Mar 20, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Exploiting vulnerabilities of deep neural networks for privacy protection	Jul 19, 2020	Adversarial AttackQuantization	CodeCode Available	5
Deep generative models as an adversarial attack strategy for tabular machine learning	Sep 19, 2024	Adversarial Attack	CodeCode Available	5
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques	Dec 3, 2020	Adversarial AttackData Augmentation	CodeCode Available	5
Exploring the Vulnerability of Natural Language Processing Models via Universal Adversarial Texts	Dec 1, 2021	Adversarial Attacktext-classification	CodeCode Available	5
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness	Nov 10, 2023	Adversarial AttackDiversity	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
Data-Driven Subsampling in the Presence of an Adversarial Actor	Jan 7, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Data-Driven Falsification of Cyber-Physical Systems	May 6, 2025	Adversarial AttackAutonomous Vehicles	CodeCode Available	5
Explainable and Safe Reinforcement Learning for Autonomous Air Mobility	Nov 24, 2022	Adversarial AttackDeep Reinforcement Learning	CodeCode Available	5
Excess Capacity and Backdoor Poisoning	Sep 2, 2021	Adversarial AttackBackdoor Attack	CodeCode Available	5
Adversarial Attacks on Large Language Models Using Regularized Relaxation	Oct 24, 2024	Adversarial Attackvalid	CodeCode Available	5
Expanding Scope: Adapting English Adversarial Attacks to Chinese	Jun 8, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	5
Explainable Graph Neural Networks Under Fire	Jun 10, 2024	Adversarial Attack	CodeCode Available	5
Adversarial Attack via Dual-Stage Network Erosion	Jan 1, 2022	Adversarial Attack	CodeCode Available	5
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice	May 30, 2021	Adversarial AttackAnomaly Detection	CodeCode Available	5
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders	Jul 19, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	5
DAmageNet: A Universal Adversarial Dataset	Dec 16, 2019	Adversarial Attack	CodeCode Available	5
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks	Jul 12, 2021	Adversarial Attack	CodeCode Available	5
Exacerbating Algorithmic Bias through Fairness Attacks	Dec 16, 2020	Adversarial AttackBIG-bench Machine Learning	CodeCode Available	5

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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