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 1426–1450 of 1808 papers

TitleStatusHype
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