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 10761100 of 1808 papers

TitleStatusHype
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and DefenseCode0
DTA: Physical Camouflage Attacks using Differentiable Transformation Network0
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack0
Efficient universal shuffle attack for visual object tracking0
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training0
Block-Sparse Adversarial Attack to Fool Transformer-Based Text ClassifiersCode0
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems0
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks0
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art0
Detecting Adversarial Perturbations in Multi-Task PerceptionCode0
Adversarial attacks on neural networks through canonical Riemannian foliationsCode0
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey0
Critical Checkpoints for Evaluating Defence Models Against Adversarial Attack and Robustness0
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias0
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing0
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack0
Attacking c-MARL More Effectively: A Data Driven Approach0
Adversarial Attack and Defense for Non-Parametric Two-Sample TestsCode0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons0
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning0
Robust Unpaired Single Image Super-Resolution of Faces0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
5AdvTraining [madry2018]Attack: PGD2048.44Unverified
6TRADES [zhang2019b]Attack: PGD2045.9Unverified
7XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified