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 901–950 of 1808 papers

TitleStatusHype
Semantic Adversarial Attacks on Face Recognition through Significant Attributes—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
Targeted Attacks on Timeseries Forecasting—0
Attacking Important Pixels for Anchor-free Detectors—0
DODEM: DOuble DEfense Mechanism Against Adversarial Attacks Towards Secure Industrial Internet of Things Analytics—0
On the feasibility of attacking Thai LPR systems with adversarial examples—0
On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense—0
Availability Adversarial Attack and Countermeasures for Deep Learning-based Load ForecastingCode0
Frequency-aware GAN for Adversarial Manipulation Generation—0
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation—0
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing—0
Explaining Adversarial Robustness of Neural Networks from Clustering Effect PerspectiveCode0
ExploreADV: Towards exploratory attack for Neural Networks—0
F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives—0
Angelic Patches for Improving Third-Party Object Detector PerformanceCode0
LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions—0
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection—0
Towards Transferable Targeted Adversarial ExamplesCode0
Transferable Adversarial Attack for Both Vision Transformers and Convolutional Networks via Momentum Integrated Gradients—0
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence—0
Multi-head Uncertainty Inference for Adversarial Attack Detection—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks—0
Adversarial Attacks and Defences for Skin Cancer Classification—0
Object-fabrication Targeted Attack for Object Detection—0
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection—0
General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments—0
Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and RegularizationCode0
Targeted Adversarial Attacks against Neural Network Trajectory Predictors—0
Pareto Regret Analyses in Multi-objective Multi-armed Bandit—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
Data Poisoning Attack Aiming the Vulnerability of Continual Learning—0
Foiling Explanations in Deep Neural NetworksCode0
Explainable and Safe Reinforcement Learning for Autonomous Air MobilityCode0
Benchmarking Adversarially Robust Quantum Machine Learning at Scale—0
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples—0
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node ImplantationCode0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation—0
Generating Textual Adversaries with Minimal PerturbationCode0
Robust Smart Home Face Recognition under Starving Federated DataCode0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
Logits are predictive of network typeCode0
Symmetric Saliency-based Adversarial Attack To Speaker Identification—0
Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation—0
TASA: Deceiving Question Answering Models by Twin Answer Sentences AttackCode0
LP-BFGS attack: An adversarial attack based on the Hessian with limited pixelsCode0
A White-Box Adversarial Attack Against a Digital Twin—0
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
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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