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 1201–1225 of 1808 papers

TitleStatusHype
Optimal Cost Constrained Adversarial Attacks For Multiple Agent Systems—0
Optimal Transport Classifier: Defending Against Adversarial Attacks by Regularized Deep Embedding—0
Optimizing Key-Selection for Face-based One-Time Biometrics via Morphing—0
Adversarially Robust Conformal Prediction—0
OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization—0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense—0
A Deep Genetic Programming based Methodology for Art Media Classification Robust to Adversarial Perturbations—0
Adversarially Robust Classification by Conditional Generative Model Inversion—0
Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks—0
Overlap-Aware Feature Learning for Robust Unsupervised Domain Adaptation for 3D Semantic Segmentation—0
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels—0
Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation—0
PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN—0
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks—0
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation—0
Pareto Regret Analyses in Multi-objective Multi-armed Bandit—0
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack—0
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds—0
Towards Robustness of Deep Neural Networks via Regularization—0
Adversarial Learning of Deepfakes in Accounting—0
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain—0
Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions—0
Towards Robust Neural Image Compression: Adversarial Attack and Model Finetuning—0
PB-UAP: Hybrid Universal Adversarial Attack For Image Segmentation—0
Towards Robust Speech-to-Text 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