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 126–150 of 1808 papers

TitleStatusHype
R&R: Metric-guided Adversarial Sentence GenerationCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
Benchmarking Adversarial Robustness on Image ClassificationCode1
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial PerturbationCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake DetectionCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
Adversarial Learning for Robust Deep ClusteringCode1
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
BERT-ATTACK: Adversarial Attack Against BERT Using BERTCode1
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness VerificationCode1
Adversarial Attacks on ML Defense Models CompetitionCode1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
3D Adversarial Attacks Beyond Point CloudCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning ModelsCode1
Adversarial Ranking Attack and DefenseCode1
Fooling the Image Dehazing Models by First Order GradientCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
Certifying LLM Safety against Adversarial PromptingCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
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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