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

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Showing 701–750 of 1808 papers

TitleStatusHype
Object-fabrication Targeted Attack for Object Detection—0
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection—0
HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable DesignCode1
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
Imperceptible Adversarial Attack via Invertible Neural NetworksCode1
Foiling Explanations in Deep Neural NetworksCode0
SAGA: Spectral Adversarial Geometric Attack on 3D MeshesCode1
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
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box AttackCode1
Ignore Previous Prompt: Attack Techniques For Language ModelsCode2
Person Text-Image Matching via Text-Feature Interpretability Embedding and External Attack Node ImplantationCode0
T-SEA: Transfer-based Self-Ensemble Attack on Object DetectionCode1
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation—0
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning—0
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing AttackCode1
Generating Textual Adversaries with Minimal PerturbationCode0
Robust Smart Home Face Recognition under Starving Federated DataCode0
Preserving Semantics in Textual Adversarial AttacksCode1
Are AlphaZero-like Agents Robust to Adversarial Perturbations?Code1
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
Logits are predictive of network typeCode0
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency DomainCode1
Rethinking Image Restoration for Object DetectionCode1
Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality MetricsCode1
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords SubstitutionCode1
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
Effective Targeted Attacks for Adversarial Self-Supervised Learning—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Probabilistic Categorical Adversarial Attack & Adversarial Training—0
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual ExplanationsCode0
Object-Attentional Untargeted Adversarial Attack—0
Dynamics-aware Adversarial Attack of Adaptive Neural NetworksCode0
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy GradientCode0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial PerturbationCode1
Adversarial Attack Against Image-Based Localization Neural Networks—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—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