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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 161–170 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
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
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
A Generative Victim Model for Segmentation	Dec 10, 2024	Adversarial AttackImage Generation	—Unverified	0
AHSG: Adversarial Attack on High-level Semantics in Graph Neural Networks	Dec 10, 2024	Adversarial AttackGraph Learning	—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

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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