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 151175 of 1808 papers

TitleStatusHype
MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin StatisticCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social NetworksCode1
Smart App Attack: Hacking Deep Learning Models in Android AppsCode1
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based RetrievalCode1
Fusing Event-based and RGB camera for Robust Object Detection in Adverse ConditionsCode1
StyleFool: Fooling Video Classification Systems via Style TransferCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Alleviating Adversarial Attacks on Variational Autoencoders with MCMCCode1
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacksCode1
Frequency-driven Imperceptible Adversarial Attack on Semantic SimilarityCode1
Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural PhenomenonCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
Random Walks for Adversarial MeshesCode1
Universal Adversarial Examples in Remote Sensing: Methodology and BenchmarkCode1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) frameworkCode1
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?Code1
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagationCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
On Adversarial Robustness of Trajectory Prediction for Autonomous VehiclesCode1
Towards Transferable Unrestricted Adversarial Examples with Minimum ChangesCode1
Exploring Effective Data for Surrogate Training Towards Black-Box AttackCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Towards Efficient Data Free Black-Box Adversarial AttackCode1
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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