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 1151–1175 of 1808 papers

TitleStatusHype
Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning—0
Object-Attentional Untargeted Adversarial Attack—0
Object-fabrication Targeted Attack for Object Detection—0
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline—0
Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs—0
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks—0
On Data Augmentation and Adversarial Risk: An Empirical Analysis—0
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack—0
Adversarial Patch Attacks on Monocular Depth Estimation Networks—0
One for Many: an Instagram inspired black-box adversarial attack—0
One-Index Vector Quantization Based Adversarial Attack on Image Classification—0
Adversarial optimization leads to over-optimistic security-constrained dispatch, but sampling can help—0
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention—0
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers—0
Adversarial Neon Beam: A Light-based Physical Attack to DNNs—0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System—0
Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness—0
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models—0
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration—0
On-Manifold Projected Gradient Descent—0
On Neural Network approximation of ideal adversarial attack and convergence of adversarial training—0
Towards more transferable adversarial attack in black-box manner—0
Adversarial Attacks and Defenses: An Interpretation Perspective—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