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 1301–1325 of 1808 papers

TitleStatusHype
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations—0
Improving Robustness of Task Oriented Dialog Systems—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing—0
Improving the Transferability of Adversarial Examples by Inverse Knowledge Distillation—0
Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation—0
Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing—0
Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration—0
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE—0
Influence Based Defense Against Data Poisoning Attacks in Online Learning—0
"Influence Sketching": Finding Influential Samples In Large-Scale Regressions—0
Information Importance-Aware Defense against Adversarial Attack for Automatic Modulation Classification:An XAI-Based Approach—0
Inline Detection of DGA Domains Using Side Information—0
Input Hessian Regularization of Neural Networks—0
Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain—0
Input-specific Attention Subnetworks for Adversarial Detection—0
Input-specific Attention Subnetworks for Adversarial Detection—0
Intermediate Level Adversarial Attack for Enhanced Transferability—0
Intermediate Outputs Are More Sensitive Than You Think—0
Internal Wasserstein Distance for Adversarial Attack and Defense—0
Interpolation between CNNs and ResNets—0
Interpreting and Evaluating Neural Network Robustness—0
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network—0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search—0
MF-CLIP: Leveraging CLIP as Surrogate Models for No-box Adversarial Attacks—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