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 1651–1700 of 1808 papers

TitleStatusHype
Revisiting DeepFool: generalization and improvementCode0
Adversarial Attack via Dual-Stage Network ErosionCode0
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit CalibrationCode0
Logits are predictive of network typeCode0
Look Closer to Your Enemy: Learning to Attack via Teacher-Student MimickingCode0
LookHere: Vision Transformers with Directed Attention Generalize and ExtrapolateCode0
AdjointDEIS: Efficient Gradients for Diffusion ModelsCode0
LP-BFGS attack: An adversarial attack based on the Hessian with limited pixelsCode0
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical WorldCode0
Adversarial Attack Generation Empowered by Min-Max OptimizationCode0
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based VisionCode0
Susceptibility of Adversarial Attack on Medical Image Segmentation ModelsCode0
RoBIC: A benchmark suite for assessing classifiers robustnessCode0
Disrupting Adversarial Transferability in Deep Neural NetworksCode0
A New Perspective on Stabilizing GANs training: Direct Adversarial TrainingCode0
Malafide: a novel adversarial convolutive noise attack against deepfake and spoofing detection systemsCode0
Unfooling Perturbation-Based Post Hoc ExplainersCode0
Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology ImagingCode0
SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural NetworksCode0
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute ModelsCode0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial AttacksCode0
Towards Safe Synthetic Image Generation On the Web: A Multimodal Robust NSFW Defense and Million Scale DatasetCode0
Differentiable Adversarial Attacks for Marked Temporal Point ProcessesCode0
MetaAdvDet: Towards Robust Detection of Evolving Adversarial AttacksCode0
TabAttackBench: A Benchmark for Adversarial Attacks on Tabular DataCode0
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial AttacksCode0
Robust Decision Trees Against Adversarial ExamplesCode0
With Friends Like These, Who Needs Adversaries?Code0
Detecting Word Sense Disambiguation Biases in Machine Translation for Model-Agnostic Adversarial AttacksCode0
Mimic and Fool: A Task Agnostic Adversarial AttackCode0
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion ModelsCode0
Using BERT Encoding to Tackle the Mad-lib Attack in SMS Spam DetectionCode0
BERTops: Studying BERT Representations under a Topological LensCode0
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary AttackCode0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy GradientCode0
Detecting Adversarial Perturbations in Multi-Task PerceptionCode0
Robust Fair Clustering: A Novel Fairness Attack and Defense FrameworkCode0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationCode0
BEARD: Benchmarking the Adversarial Robustness for Dataset DistillationCode0
Robustness-aware Automatic Prompt OptimizationCode0
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification ModelsCode0
Targeted Adversarial Attacks against Neural Machine TranslationCode0
Model-Agnostic Defense for Lane Detection against Adversarial AttackCode0
Adversarial Privacy-preserving FilterCode0
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision MakingCode0
2D-Malafide: Adversarial Attacks Against Face Deepfake Detection SystemsCode0
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttackCode0
Detecting Adversarial Examples in Batches -- a geometrical approachCode0
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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