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 601–650 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
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—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
Feature Separation and Recalibration for Adversarial RobustnessCode1
Effective black box adversarial attack with handcrafted kernels—0
Improved Adversarial Training Through Adaptive Instance-wise Loss SmoothingCode0
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
Revisiting DeepFool: generalization and improvementCode0
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face RecognitionCode0
Wasserstein Adversarial Examples on Univariant Time Series Data—0
Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense—0
Translate your gibberish: black-box adversarial attack on machine translation systemsCode0
NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models—0
Resilient Dynamic Average Consensus based on Trusted agents—0
Constrained Adversarial Learning for Automated Software Testing: a literature review—0
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?Code0
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network—0
Adaptive Local Adversarial Attacks on 3D Point Clouds for Augmented Reality—0
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey—0
Do we need entire training data for adversarial training?—0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems—0
Feature Unlearning for Pre-trained GANs and VAEs—0
Identification of Systematic Errors of Image Classifiers on Rare Subgroups—0
Decision-BADGE: Decision-based Adversarial Batch Attack with Directional Gradient EstimationCode0
Logit Margin Matters: Improving Transferable Targeted Adversarial Attack by Logit CalibrationCode0
Adversarial Sampling for Fairness Testing in Deep Neural Network—0
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models—0
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems—0
Targeted Adversarial Attacks against Neural Machine TranslationCode0
Adversarial Attack with Raindrops—0
Contextual adversarial attack against aerial detection in the physical world—0
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation—0
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks—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