SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 481–490 of 1808 papers

TitleStatusHype
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
NMT-Obfuscator Attack: Ignore a sentence in translation with only one wordCode0
DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning—0
BEARD: Benchmarking the Adversarial Robustness for Dataset DistillationCode0
Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach—0
Chain Association-based Attacking and Shielding Natural Language Processing Systems—0
Neural Fingerprints for Adversarial Attack DetectionCode0
Attention Masks Help Adversarial Attacks to Bypass Safety DetectorsCode0
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models—0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified