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 301–350 of 1808 papers

TitleStatusHype
Renofeation: A Simple Transfer Learning Method for Improved Adversarial RobustnessCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object TrackingCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Square Attack: a query-efficient black-box adversarial attack via random searchCode1
Nesterov Accelerated Gradient and Scale Invariance for Adversarial AttacksCode1
Natural Adversarial ExamplesCode1
Provably Robust Deep Learning via Adversarially Trained Smoothed ClassifiersCode1
High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksCode1
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object TrackingCode1
Adversarial Training for Free!Code1
Wasserstein Adversarial Examples via Projected Sinkhorn IterationsCode1
On Evaluating Adversarial RobustnessCode1
Theoretically Principled Trade-off between Robustness and AccuracyCode1
Distributionally Adversarial AttackCode1
Local Gradients Smoothing: Defense against localized adversarial attacksCode1
Generalizable Data-free Objective for Crafting Universal Adversarial PerturbationsCode1
Towards Deep Learning Models Resistant to Adversarial AttacksCode1
Adversarial Examples for Semantic Segmentation and Object DetectionCode1
Deep Variational Information BottleneckCode1
3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving—0
VIP: Visual Information Protection through Adversarial Attacks on Vision-Language ModelsCode0
Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF InfeasibleCode0
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage GenerationCode0
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttackCode0
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis—0
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation—0
Doppelganger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack—0
Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories—0
Alphabet Index Mapping: Jailbreaking LLMs through Semantic Dissimilarity—0
Second Order State Hallucinations for Adversarial Attack Mitigation in Formation Control of Multi-Agent Systems—0
On the existence of consistent adversarial attacks in high-dimensional linear classification—0
Unsourced Adversarial CAPTCHA: A Bi-Phase Adversarial CAPTCHA Framework—0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance—0
A look at adversarial attacks on radio waveforms from discrete latent space—0
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)—0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model ReliabilityCode0
Efficient Robust Conformal Prediction via Lipschitz-Bounded NetworksCode0
CAPAA: Classifier-Agnostic Projector-Based Adversarial AttackCode0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute Recognition—0
Seeing the Threat: Vulnerabilities in Vision-Language Models to Adversarial Attack—0
A Framework for Adversarial Analysis of Decision Support Systems Prior to Deployment—0
TabAttackBench: A Benchmark for Adversarial Attacks on Tabular DataCode0
Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion—0
TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization—0
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation—0
Towards more transferable adversarial attack in black-box manner—0
Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation—0
Temporal Consistency Constrained Transferable Adversarial Attacks with Background Mixup for Action RecognitionCode0
Show:102550
← PrevPage 7 of 37Next →
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