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

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Showing 1451–1500 of 1808 papers

TitleStatusHype
Minority Reports Defense: Defending Against Adversarial Patches—0
Transferable Perturbations of Deep Feature Distributions—0
Towards Feature Space Adversarial AttackCode1
Enabling Fast and Universal Audio Adversarial Attack Using Generative Model—0
On the Optimal Interaction Range for Multi-Agent Systems Under Adversarial Attack—0
Improved Adversarial Training via Learned Optimizer—0
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers—0
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty—0
Adversarial Attacks and Defenses: An Interpretation Perspective—0
BERT-ATTACK: Adversarial Attack Against BERT Using BERTCode1
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning—0
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space—0
Extending Adversarial Attacks to Produce Adversarial Class Probability DistributionsCode0
Towards Transferable Adversarial Attack against Deep Face Recognition—0
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images—0
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-RankingCode1
SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen CamerasCode0
BAE: BERT-based Adversarial Examples for Text ClassificationCode2
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays—0
Adversarial Imitation Attack—0
Challenging the adversarial robustness of DNNs based on error-correcting output codes—0
Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible NoisesCode1
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method—0
Motion-Excited Sampler: Video Adversarial Attack with Sparked PriorCode1
Inline Detection of DGA Domains Using Side Information—0
Frequency-Tuned Universal Adversarial Attacks—0
Using an ensemble color space model to tackle adversarial examples—0
SAD: Saliency-based Defenses Against Adversarial Examples—0
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world—0
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks—0
Search Space of Adversarial Perturbations against Image Filters—0
Real-time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems—0
Double Backpropagation for Training Autoencoders against Adversarial Attack—0
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack—0
Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation SystemsCode1
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical StudiesCode2
Applying Tensor Decomposition to image for Robustness against Adversarial Attack—0
Adversarial Ranking Attack and DefenseCode1
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
Temporal Sparse Adversarial Attack on Sequence-based Gait Recognition—0
A Bayes-Optimal View on Adversarial Examples—0
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient DescentCode0
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack—0
Undersensitivity in Neural Reading Comprehension—0
Stabilizing Differentiable Architecture Search via Perturbation-based RegularizationCode1
Adversarial Data Encryption—0
Watch out! Motion is Blurring the Vision of Your Deep Neural NetworksCode1
Renofeation: A Simple Transfer Learning Method for Improved Adversarial RobustnessCode1
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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