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 1551–1600 of 1808 papers

TitleStatusHype
Controversial stimuli: pitting neural networks against each other as models of human recognitionCode0
A New Ensemble Adversarial Attack Powered by Long-term Gradient MemoriesCode0
Black-Box Adversarial Attack with Transferable Model-based EmbeddingCode0
SMART: Skeletal Motion Action Recognition aTtack—0
Suspicion-Free Adversarial Attacks on Clustering Algorithms—0
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique—0
Adversarial Examples in Modern Machine Learning: A ReviewCode0
Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN—0
Improving Robustness of Task Oriented Dialog Systems—0
Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy—0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems—0
Patch augmentation: Towards efficient decision boundaries for neural networksCode0
White-Box Target Attack for EEG-Based BCI Regression Problems—0
Reversible Adversarial Attack based on Reversible Image Transformation—0
Who is Real Bob? Adversarial Attacks on Speaker Recognition SystemsCode0
The FEVER2.0 Shared Task—0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System—0
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization—0
Active Subspace of Neural Networks: Structural Analysis and Universal AttacksCode0
Word-level Textual Adversarial Attacking as Combinatorial OptimizationCode0
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks—0
Learning to Learn by Zeroth-Order OracleCode0
Improving Sequence Modeling Ability of Recurrent Neural Networks via SememesCode0
SPARK: Spatial-aware Online Incremental Attack Against Visual TrackingCode0
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
Real-world adversarial attack on MTCNN face detection systemCode0
On Robustness of Neural Ordinary Differential EquationsCode0
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing—0
Adversarial Learning of Deepfakes in Accounting—0
AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation—0
Yet another but more efficient black-box adversarial attack: tiling and evolution strategies—0
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural NetworksCode0
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions—0
An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack—0
Role of Spatial Context in Adversarial Robustness for Object DetectionCode0
Deep k-NN Defense against Clean-label Data Poisoning AttacksCode0
Towards Certified Defense for Unrestricted Adversarial Attacks—0
Simple and Effective Stochastic Neural Networks—0
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection—0
Learning Key Steps to Attack Deep Reinforcement Learning Agents—0
Adversarial training with perturbation generator networks—0
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness—0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images—0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex—0
THE EFFECT OF ADVERSARIAL TRAINING: A THEORETICAL CHARACTERIZATION—0
SELF-KNOWLEDGE DISTILLATION ADVERSARIAL ATTACK—0
Universal Adversarial Attack Using Very Few Test Examples—0
Defending Against Adversarial Examples by Regularized Deep Embedding—0
Robust saliency maps with distribution-preserving decoys—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