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

Showing 5175 of 1808 papers

TitleStatusHype
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Are AlphaZero-like Agents Robust to Adversarial Perturbations?Code1
A Review of Adversarial Attack and Defense for Classification MethodsCode1
Adversarial Examples for Semantic Segmentation and Object DetectionCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
Attacking Recommender Systems with Augmented User ProfilesCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box FrameworkCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
An Efficient Adversarial Attack for Tree EnsemblesCode1
Adversarial Attack on Large Scale GraphCode1
Adversarial Vulnerabilities in Large Language Models for Time Series ForecastingCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
Adversarial Attack on Graph Neural Networks as An Influence Maximization ProblemCode1
Fooling the Image Dehazing Models by First Order GradientCode1
Adversarial Attack On Yolov5 For Traffic And Road Sign DetectionCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
3D Adversarial Attacks Beyond Point CloudCode1
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
4AdvTraining [madry2018]Attack: PGD2048.44Unverified
5TRADES [zhang2019b]Attack: PGD2045.9Unverified
6XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified