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 501–550 of 1808 papers

TitleStatusHype
Certifiably Robust Variational Autoencoders—0
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation—0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Chain Association-based Attacking and Shielding Natural Language Processing Systems—0
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems—0
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers—0
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers—0
Adversarially Robust Neural Architectures—0
CharBot: A Simple and Effective Method for Evading DGA Classifiers—0
A Framework for Adversarial Analysis of Decision Support Systems Prior to Deployment—0
Adversarial Attack on Deep Product Quantization Network for Image Retrieval—0
DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques—0
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing—0
Enhancing Tracking Robustness with Auxiliary Adversarial Defense Networks—0
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization—0
Attack Type Agnostic Perceptual Enhancement of Adversarial Images—0
Adversarially robust generalization theory via Jacobian regularization for deep neural networks—0
Attack Tree Analysis for Adversarial Evasion Attacks—0
Adversarially robust deepfake media detection using fused convolutional neural network predictions—0
Adaptive Adversarial Training Does Not Increase Recourse Costs—0
Attack to Fool and Explain Deep Networks—0
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network—0
Adversarially Robust Conformal Prediction—0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples—0
Adversarially Robust Classification by Conditional Generative Model Inversion—0
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
Show:102550
← PrevPage 11 of 37Next →
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