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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 121–130 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
Distributionally Adversarial Attack	Aug 16, 2018	Adversarial Attack	CodeCode Available	1	5
An Extensive Study on Adversarial Attack against Pre-trained Models of Code	Nov 13, 2023	Adversarial Attack	CodeCode Available	1	5
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack	Dec 22, 2019	Adversarial AttackAdversarial Text	CodeCode Available	1	5
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural Networks	May 19, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box Framework	May 24, 2025	Adversarial AttackSpeech Tokenization	CodeCode Available	1	5
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation	Mar 16, 2021	Adversarial AttackObject	CodeCode Available	1	5
A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space	Dec 2, 2021	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
R&R: Metric-guided Adversarial Sentence Generation	Apr 17, 2021	Adversarial AttackGeneral Classification	CodeCode Available	1	5
Attacking Recommender Systems with Augmented User Profiles	May 17, 2020	Adversarial AttackGenerative Adversarial Network	CodeCode Available	1	5
Attacking Video Recognition Models with Bullet-Screen Comments	Oct 29, 2021	Adversarial AttackAdversarial Attack on Video Classification	CodeCode Available	1	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