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 1576–1600 of 1808 papers

TitleStatusHype
Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation—0
Cloud Adversarial Example Generation for Remote Sensing Image Classification—0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
SHARP: Search-Based Adversarial Attack for Structured Prediction—0
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples—0
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness—0
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"—0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech—0
Compressed models are NOT miniature versions of large models—0
Compressive Sensing Based Adaptive Defence Against Adversarial Images—0
Conformal Shield: A Novel Adversarial Attack Detection Framework for Automatic Modulation Classification—0
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces—0
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models—0
A Survey on Physical Adversarial Attacks against Face Recognition Systems—0
Constrained Adversarial Learning for Automated Software Testing: a literature review—0
Constrained Network Adversarial Attacks: Validity, Robustness, and Transferability—0
Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories—0
Content-based Unrestricted Adversarial Attack—0
Context-aware Adversarial Attack on Named Entity Recognition—0
Contextual adversarial attack against aerial detection in the physical world—0
A Survey on Physical Adversarial Attack in Computer Vision—0
Contrastive Weighted Learning for Near-Infrared Gaze Estimation—0
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack—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