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 951–1000 of 1808 papers

TitleStatusHype
Effective Targeted Attacks for Adversarial Self-Supervised Learning—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual ExplanationsCode0
Probabilistic Categorical Adversarial Attack & Adversarial Training—0
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
Adversarial Attack Against Image-Based Localization Neural Networks—0
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—0
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks—0
Jitter Does Matter: Adapting Gaze Estimation to New Domains—0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers—0
Robust Fair Clustering: A Novel Fairness Attack and Defense FrameworkCode0
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks—0
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack—0
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack—0
A Survey on Physical Adversarial Attack in Computer Vision—0
Activation Learning by Local Competitions—0
Fair Robust Active Learning by Joint Inconsistency—0
AdvDO: Realistic Adversarial Attacks for Trajectory Prediction—0
Adversarial Color Projection: A Projector-based Physical Attack to DNNs—0
Watch What You Pretrain For: Targeted, Transferable Adversarial Examples on Self-Supervised Speech Recognition modelsCode0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial AttackCode0
Robust Constrained Reinforcement Learning—0
Sample Complexity of an Adversarial Attack on UCB-based Best-arm Identification Policy—0
PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models—0
ADMM based Distributed State Observer Design under Sparse Sensor Attacks—0
Generate synthetic samples from tabular dataCode0
Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature RandomizationCode0
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples—0
Impact of Scaled Image on Robustness of Deep Neural Networks—0
A Black-Box Attack on Optical Character Recognition Systems—0
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy ProtectionCode0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment—0
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks—0
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search—0
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design—0
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator—0
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation—0
Design of secure and robust cognitive system for malware detection—0
Look Closer to Your Enemy: Learning to Attack via Teacher-Student MimickingCode0
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception—0
Versatile Weight Attack via Flipping Limited BitsCode0
Rethinking Textual Adversarial Defense for Pre-trained Language Models—0
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks—0
Decorrelative Network Architecture for Robust Electrocardiogram ClassificationCode0
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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