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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 291–300 of 1808 papers

Title	Date	Tasks	Status	Hype
On Discrete Prompt Optimization for Diffusion Models	Jun 27, 2024	Adversarial AttackPrompt Engineering	CodeCode Available	2
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems	Jun 25, 2024	Adversarial AttackMulti-agent Reinforcement Learning	—Unverified	0
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification	Jun 24, 2024	Adversarial AttackClassification	—Unverified	0
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI	Jun 22, 2024	Adversarial Attack	—Unverified	0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning	Jun 19, 2024	Adversarial AttackAdversarial Defense	—Unverified	0
AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization	Jun 19, 2024	Adversarial Attack	—Unverified	0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation	Jun 18, 2024	Adversarial AttackSemantic Similarity	—Unverified	0
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks	Jun 18, 2024	Adversarial AttackIntrusion Detection	CodeCode Available	0
Imperceptible Face Forgery Attack via Adversarial Semantic Mask	Jun 16, 2024	Adversarial Attack	CodeCode Available	0
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models	Jun 16, 2024	Adversarial AttackBenchmarking	CodeCode Available	2

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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