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

Showing 5175 of 1808 papers

TitleStatusHype
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
A Review of Adversarial Attack and Defense for Classification MethodsCode1
Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial AttackCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query AttacksCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Augmented Lagrangian Adversarial AttacksCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
3D Gaussian Splat VulnerabilitiesCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
Adversarial Ranking Attack and DefenseCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
Fooling the Image Dehazing Models by First Order GradientCode1
Show:102550
← PrevPage 3 of 73Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
4AdvTraining [madry2018]Attack: PGD2048.44Unverified
5TRADES [zhang2019b]Attack: PGD2045.9Unverified
6XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified