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 1501–1525 of 1808 papers

TitleStatusHype
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
Solving Non-Convex Non-Differentiable Min-Max Games using Proximal Gradient Method—0
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
Applying Tensor Decomposition to image for Robustness against Adversarial Attack—0
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
Adversarial Data Encryption—0
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels—0
DANCE: Enhancing saliency maps using decoysCode0
Practical Fast Gradient Sign Attack against Mammographic Image Classifier—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet20Test Accuracy89.9589.95(1)Community Verified
2Xu et al.Attack: PGD2078.68—Unverified
33-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
4TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
5AdvTraining [madry2018]Attack: PGD2048.44—Unverified
6TRADES [zhang2019b]Attack: PGD2045.9—Unverified
7XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified