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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 281–290 of 1808 papers

Title	Date	Tasks	Status	Hype	Score
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
Adversarial Attacks on ML Defense Models Competition	Oct 15, 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
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain	Aug 19, 2021	Adversarial AttackData Augmentation	CodeCode Available	1	5
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed Gradient	Aug 10, 2023	Action RecognitionActivity Recognition	CodeCode Available	1	5
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking	May 27, 2019	Adversarial AttackAutonomous Driving	CodeCode Available	1	5
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models	Oct 23, 2023	Adversarial AttackBlocking	CodeCode Available	1	5
Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan Script	Dec 17, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	1	5
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model	Aug 17, 2024	Adversarial AttackSpeaker Verification	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