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 501–525 of 1808 papers

TitleStatusHype
Certifiably Robust Variational Autoencoders—0
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation—0
Design of secure and robust cognitive system for malware detection—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
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking—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
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
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training—0
Adversarial Learning of Deepfakes in Accounting—0
Attacking Perceptual Similarity Metrics—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