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 1126–1150 of 1808 papers

TitleStatusHype
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models—0
TDGIA:Effective Injection Attacks on Graph Neural NetworksCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
Sparse and Imperceptible Adversarial Attack via a Homotopy AlgorithmCode0
On Improving Adversarial Transferability of Vision TransformersCode1
Adversarial Attack and Defense in Deep RankingCode1
Transferable Adversarial Examples for Anchor Free Object Detection—0
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial AttackCode0
Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial AttackCode0
Transferable Sparse Adversarial AttackCode1
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance SacrificeCode0
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness—0
Reducing DNN Properties to Enable Falsification with Adversarial AttacksCode0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
Adversarial Attack Driven Data Augmentation for Accurate And Robust Medical Image Segmentation—0
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating DeepfakesCode1
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems—0
Local Aggressive Adversarial Attacks on 3D Point CloudCode0
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
Improving Adversarial Transferability with Gradient RefiningCode1
Poisoning MorphNet for Clean-Label Backdoor Attack to Point Clouds—0
Automated Decision-based Adversarial Attacks—0
Self-Supervised Adversarial Example Detection by Disentangled Representation—0
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine 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