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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 1091–1100 of 1808 papers

Title	Date	Tasks	Status	Hype
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing	Mar 21, 2021	Adversarial Attacknamed-entity-recognition	—Unverified	0
Agents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks	Mar 31, 2025	Adversarial AttackLarge Language Model	—Unverified	0
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification	Feb 3, 2023	Adversarial AttackClassification	—Unverified	0
TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models	Aug 26, 2024	Adversarial Attack	—Unverified	0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks	Jun 17, 2019	Adversarial AttackAdversarial Robustness	—Unverified	0
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection	Jan 1, 2023	Adversarial AttackDynamic neural networks	—Unverified	0
The Double-Edged Sword of Input Perturbations to Robust Accurate Fairness	Apr 1, 2024	Adversarial AttackFairness	—Unverified	0
THE EFFECT OF ADVERSARIAL TRAINING: A THEORETICAL CHARACTERIZATION	Sep 25, 2019	Adversarial Attack	—Unverified	0
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI	Jun 22, 2024	Adversarial Attack	—Unverified	0
The Efficacy of SHIELD under Different Threat Models	Feb 1, 2019	Adversarial Attackimage-classification	—Unverified	0

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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