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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 1711–1720 of 1808 papers

Title	Date	Tasks	Status	Hype
Deflecting Adversarial Attacks with Pixel Deflection	Jan 26, 2018	Adversarial Attack	CodeCode Available	0
Multi-Granularity Tibetan Textual Adversarial Attack Method Based on Masked Language Model	Dec 3, 2024	Adversarial AttackHate Speech Detection	CodeCode Available	0
DANCE: Enhancing saliency maps using decoys	Feb 3, 2020	Adversarial Attack	CodeCode Available	0
Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection	Aug 22, 2023	Adversarial Attack	CodeCode Available	0
Towards Transferable Targeted Adversarial Examples	Jan 1, 2023	Adversarial Attack	CodeCode Available	0
TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack	Oct 27, 2022	Adversarial AttackQuestion Answering	CodeCode Available	0
Adversarial Attacks on Large Language Models Using Regularized Relaxation	Oct 24, 2024	Adversarial Attackvalid	CodeCode Available	0
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty	Nov 5, 2020	Adversarial AttackBenchmarking	CodeCode Available	0
Task and Model Agnostic Adversarial Attack on Graph Neural Networks	Dec 25, 2021	Adversarial AttackQ-Learning	CodeCode Available	0
T-BFA: Targeted Bit-Flip Adversarial Weight Attack	Jul 24, 2020	Adversarial AttackCPU	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