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 526–550 of 1808 papers

TitleStatusHype
Adversarial Attack on Deep Cross-Modal Hamming Retrieval—0
Adversarial Learning of Deepfakes in Accounting—0
Attacking Perceptual Similarity Metrics—0
Attacking Important Pixels for Anchor-free Detectors—0
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain—0
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation—0
Enhancing Transformation-based Defenses using a Distribution Classifier—0
Attack-Agnostic Adversarial Detection—0
A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays—0
Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions—0
AT-GAN: An Adversarial Generative Model for Non-constrained Adversarial Examples—0
Semantically Stealthy Adversarial Attacks against Segmentation Models—0
Device-aware Optical Adversarial Attack for a Portable Projector-camera System—0
AT-GAN: An Adversarial Generator Model for Non-constrained Adversarial Examples—0
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions—0
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge—0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech—0
Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors—0
Adaptive Adversarial Attack on Scene Text Recognition—0
DFT-Based Adversarial Attack Detection in MRI Brain Imaging: Enhancing Diagnostic Accuracy in Alzheimer's Case Studies—0
A Survey on Physical Adversarial Attacks against Face Recognition Systems—0
A Survey on Physical Adversarial Attack in Computer Vision—0
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies—0
A Study on the Efficiency and Generalization of Light Hybrid Retrievers—0
Adversarial Imitation Attack—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