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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 41–50 of 1808 papers

Title	Date	Tasks	Status	Hype
Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan Script	Dec 17, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	1
A2RNet: Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion	Dec 13, 2024	Adversarial AttackInfrared And Visible Image Fusion	CodeCode Available	1
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting	Dec 11, 2024	Adversarial AttackTime Series	CodeCode Available	1
Exploiting the Index Gradients for Optimization-Based Jailbreaking on Large Language Models	Dec 11, 2024	Adversarial Attack	CodeCode Available	1
Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection	Dec 2, 2024	Adversarial AttackFace Detection	CodeCode Available	1
Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack	Nov 4, 2024	Adversarial AttackDiversity	CodeCode Available	1
Transferable Adversarial Attacks on SAM and Its Downstream Models	Oct 26, 2024	Adversarial Attack	CodeCode Available	1
Malacopula: adversarial automatic speaker verification attacks using a neural-based generalised Hammerstein model	Aug 17, 2024	Adversarial AttackSpeaker Verification	CodeCode Available	1
Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness	Aug 8, 2024	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics	Aug 2, 2024	Adversarial AttackAdversarial Purification	CodeCode Available	1

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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