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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 1491–1500 of 1808 papers

Title	Date	Tasks	Status	Hype
Enhancing Neural Models with Vulnerability via Adversarial Attack	Dec 1, 2020	Adversarial AttackSentence	CodeCode Available	0
Towards adversarial robustness verification of no-reference image-and video-quality metrics	Dec 30, 2023	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction	Nov 22, 2019	Adversarial Attackimage-classification	CodeCode Available	0
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection	Aug 23, 2022	Adversarial Attack	CodeCode Available	0
High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image Synthesis	Sep 12, 2024	Adversarial AttackAdversarial Purification	CodeCode Available	0
Practical Relative Order Attack in Deep Ranking	Mar 9, 2021	Adversarial AttackTriplet	CodeCode Available	0
An adversarial attack approach for eXplainable AI evaluation on deepfake detection models	Dec 8, 2023	Adversarial AttackDeepFake Detection	CodeCode Available	0
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack	Sep 24, 2019	Adversarial AttackAdversarial Robustness	CodeCode Available	0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability	Jun 9, 2025	Adversarial AttackAdversarial Robustness	CodeCode Available	0
How Private Is Your RL Policy? An Inverse RL Based Analysis Framework	Dec 10, 2021	Adversarial AttackAutonomous Driving	CodeCode Available	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