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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 61–70 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Attack for RGB-Event based Visual Object Tracking	Apr 19, 2025	Adversarial AttackObject Tracking	CodeCode Available	0
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models	Apr 19, 2025	Adversarial AttackAdversarial Defense	—Unverified	0
Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation	Apr 18, 2025	Adversarial Attack	—Unverified	0
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification	Apr 17, 2025	Adversarial AttackDeep Learning	CodeCode Available	0
SemDiff: Generating Natural Unrestricted Adversarial Examples via Semantic Attributes Optimization in Diffusion Models	Apr 16, 2025	Adversarial Attack	—Unverified	0
Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale Dataset	Apr 16, 2025	Adversarial AttackImage Generation	CodeCode Available	0
Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization	Apr 13, 2025	Adversarial Attack	—Unverified	0
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks	Apr 11, 2025	Adversarial AttackAdversarial Robustness	—Unverified	0
Towards Calibration Enhanced Network by Inverse Adversarial Attack	Apr 8, 2025	Adversarial AttackOptical Character Recognition	—Unverified	0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability	Apr 7, 2025	Adversarial AttackAdversarial Robustness	—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