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 1401–1450 of 1808 papers

TitleStatusHype
Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method—0
Robustness of Selected Learning Models under Label-Flipping Attack—0
Robust Optimal Power Flow Against Adversarial Attacks: A Tri-Level Optimization Approach—0
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms—0
Robust Physical-World Attacks on Face Recognition—0
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression—0
When Side-Channel Attacks Break the Black-Box Property of Embedded Artificial Intelligence—0
Trustworthy Actionable Perturbations—0
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search—0
Robust saliency maps with distribution-preserving decoys—0
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition—0
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness—0
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack—0
Robust Superpixel-Guided Attentional Adversarial Attack—0
Robust Text CAPTCHAs Using Adversarial Examples—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
Adversarial Attacks on Deep Learning Based mmWave Beam Prediction in 5G and Beyond—0
Robust Transfer Learning with Pretrained Language Models through Adapters—0
Robust Unpaired Single Image Super-Resolution of Faces—0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective—0
Rogue Cell: Adversarial Attack and Defense in Untrusted O-RAN Setup Exploiting the Traffic Steering xApp—0
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints—0
Adversarial Attacks on Deep Graph Matching—0
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation—0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex—0
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples—0
SAD: Saliency-based Defenses Against Adversarial Examples—0
Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection—0
Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors—0
Adversarial attacks on audio source separation—0
A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability—0
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks—0
Adversarial attacks on an optical neural network—0
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies—0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation—0
Salient Information Preserving Adversarial Training Improves Clean and Robust Accuracy—0
Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets—0
Sample Complexity of an Adversarial Attack on UCB-based Best-arm Identification Policy—0
Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks—0
SAR-AE-SFP: SAR Imagery Adversarial Example in Real Physics domain with Target Scattering Feature Parameters—0
Undersensitivity in Neural Reading Comprehension—0
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers—0
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning—0
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator—0
Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution—0
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses—0
Scaling Laws for Black box Adversarial Attacks—0
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization—0
Understanding Model Ensemble in Transferable Adversarial Attack—0
Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings—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