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 1101–1150 of 1808 papers

TitleStatusHype
Discriminator-Free Generative Adversarial AttackCode1
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition—0
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based AttacksCode0
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam DetectionCode0
EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial AttacksCode0
Noise-based cyberattacks generating fake P300 waves in brain–computer interfacesCode0
Learning to Detect Adversarial Examples Based on Class Scores—0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks—0
Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples—0
DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural NetworksCode0
Bio-Inspired Adversarial Attack Against Deep Neural Networks—0
In-distribution adversarial attacks on object recognition models using gradient-free searchCode0
Attack Transferability Characterization for Adversarially Robust Multi-label ClassificationCode0
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack—0
Adversarial Attack on Graph Neural Networks as An Influence Maximization ProblemCode1
Attack to Fool and Explain Deep Networks—0
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents—0
Is It Time to Redefine the Classification Task for Deep Learning Systems?—0
Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions—0
Limited Budget Adversarial Attack Against Online Image Stream—0
Light Lies: Optical Adversarial Attack—0
Now You See It, Now You Dont: Adversarial Vulnerabilities in Computational Pathology—0
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