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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 461–470 of 1808 papers

Title	Date	Tasks	Status	Hype
Adversarial Robustness through Dynamic Ensemble Learning	Dec 20, 2024	Adversarial AttackAdversarial Robustness	—Unverified	0
Watertox: The Art of Simplicity in Universal Attacks A Cross-Model Framework for Robust Adversarial Generation	Dec 20, 2024	Adversarial AttackDiversity	—Unverified	0
Adversarially robust generalization theory via Jacobian regularization for deep neural networks	Dec 17, 2024	Adversarial Attack	—Unverified	0
Unpacking the Resilience of SNLI Contradiction Examples to Attacks	Dec 15, 2024	Adversarial Attack	CodeCode Available	0
RAT: Adversarial Attacks on Deep Reinforcement Agents for Targeted Behaviors	Dec 14, 2024	Adversarial AttackDeep Reinforcement Learning	—Unverified	0
AHSG: Adversarial Attack on High-level Semantics in Graph Neural Networks	Dec 10, 2024	Adversarial AttackGraph Learning	—Unverified	0
A Generative Victim Model for Segmentation	Dec 10, 2024	Adversarial AttackImage Generation	—Unverified	0
Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection	Dec 9, 2024	Adversarial Attack	—Unverified	0
From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation	Dec 7, 2024	Adversarial AttackExplainable artificial intelligence	CodeCode Available	0
Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts?	Dec 4, 2024	Adversarial Attack	—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