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
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
Universal Adversarial Attack Using Very Few Test Examples—0
Learning Key Steps to Attack Deep Reinforcement Learning Agents—0
Robust saliency maps with distribution-preserving decoys—0
SELF-KNOWLEDGE DISTILLATION ADVERSARIAL ATTACK—0
DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images—0
Simple and Effective Stochastic Neural Networks—0
THE EFFECT OF ADVERSARIAL TRAINING: A THEORETICAL CHARACTERIZATION—0
Towards Certified Defense for Unrestricted Adversarial Attacks—0
Adversarial training with perturbation generator networks—0
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex—0
Defending Against Adversarial Examples by Regularized Deep Embedding—0
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness—0
Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection—0
Sign-OPT: A Query-Efficient Hard-label Adversarial AttackCode0
COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection—0
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation—0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks—0
Adversarial Attacks and Defenses in Images, Graphs and Text: A ReviewCode2
Natural Language Adversarial Defense through Synonym EncodingCode0
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms—0
Adversarial Attack on Skeleton-based Human Action Recognition—0
An Empirical Investigation of Randomized Defenses against Adversarial AttacksCode0
BOSH: An Efficient Meta Algorithm for Decision-based Attacks—0
Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification—0
FDA: Feature Disruptive AttackCode0
STA: Adversarial Attacks on Siamese Trackers—0
Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the TowerCode0
AdvHat: Real-world adversarial attack on ArcFace Face ID systemCode0
Nesterov Accelerated Gradient and Scale Invariance for Adversarial AttacksCode1
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation—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