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 776–800 of 1808 papers

TitleStatusHype
Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and DefenseCode1
Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature RandomizationCode0
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples—0
Impact of Scaled Image on Robustness of Deep Neural Networks—0
A Black-Box Attack on Optical Character Recognition Systems—0
Semantic Preserving Adversarial Attack Generation with Autoencoder and Genetic Algorithm—0
Unrestricted Black-box Adversarial Attack Using GAN with Limited QueriesCode1
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy ProtectionCode0
Different Spectral Representations in Optimized Artificial Neural Networks and BrainsCode0
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks—0
UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QACode1
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment—0
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search—0
InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a TeeCode1
MENLI: Robust Evaluation Metrics from Natural Language InferenceCode1
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design—0
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless NetworksCode1
Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generator—0
Design of secure and robust cognitive system for malware detection—0
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation—0
Look Closer to Your Enemy: Learning to Attack via Teacher-Student MimickingCode0
LGV: Boosting Adversarial Example Transferability from Large Geometric VicinityCode1
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception—0
Versatile Weight Attack via Flipping Limited BitsCode0
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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