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 601–625 of 1808 papers

TitleStatusHype
White-Box Multi-Objective Adversarial Attack on Dialogue GenerationCode1
New Adversarial Image Detection Based on Sentiment AnalysisCode0
Boosting Adversarial Transferability via Fusing Logits of Top-1 Decomposed FeatureCode0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples—0
Evaluating Adversarial Robustness on Document Image Classification—0
Wavelets Beat Monkeys at Adversarial Robustness—0
Towards the Transferable Audio Adversarial Attack via Ensemble Methods—0
Combining Generators of Adversarial Malware Examples to Increase Evasion RateCode0
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality MetricsCode0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
Generating Adversarial Attacks in the Latent Space—0
Robust Deep Learning Models Against Semantic-Preserving Adversarial Attack—0
GradMDM: Adversarial Attack on Dynamic Networks—0
To be Robust and to be Fair: Aligning Fairness with Robustness—0
Fooling the Image Dehazing Models by First Order GradientCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Class-Conditioned Transformation for Enhanced Robust Image ClassificationCode0
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
Effective black box adversarial attack with handcrafted kernels—0
Feature Separation and Recalibration for Adversarial RobustnessCode1
Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and ChallengesCode0
Semantic Image Attack for Visual Model Diagnosis—0
State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems—0
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face RecognitionCode0
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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