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 1501–1550 of 1808 papers

TitleStatusHype
Bio-Inspired Adversarial Attack Against Deep Neural Networks—0
Biologically inspired protection of deep networks from adversarial attacks—0
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner—0
SELF-KNOWLEDGE DISTILLATION ADVERSARIAL ATTACK—0
Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving—0
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method—0
Black-box Adversarial Attacks on Commercial Speech Platforms with Minimal Information—0
Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
Adversarial Attacks in Multimodal Systems: A Practitioner's Survey—0
Self-Supervised Adversarial Example Detection by Disentangled Representation—0
Attention, Please! Adversarial Defense via Activation Rectification and Preservation—0
Black-box Adversarial ML Attack on Modulation Classification—0
Black-Box Decision based Adversarial Attack with Symmetric α-stable Distribution—0
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation—0
Black-box Targeted Adversarial Attack on Segment Anything (SAM)—0
blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness—0
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples—0
Enhancing Transformation-based Defenses using a Distribution Classifier—0
Blurring Fools the Network -- Adversarial Attacks by Feature Peak Suppression and Gaussian Blurring—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Robust Pretrained Language Models—0
Self-Supervised Representation Learning for Adversarial Attack Detection—0
Boosting Adversarial Transferability of MLP-Mixer—0
Boosting Adversarial Transferability through Enhanced Momentum—0
Boosting Adversarial Transferability using Dynamic Cues—0
Semantic Adversarial Attacks on Face Recognition through Significant Attributes—0
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization—0
Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion—0
Boosting Black-Box Adversarial Attacks with Meta Learning—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors—0
Attack Type Agnostic Perceptual Enhancement of Adversarial Images—0
Attack Tree Analysis for Adversarial Evasion Attacks—0
Attack to Fool and Explain Deep Networks—0
Attacks on State-of-the-Art Face Recognition using Attentional Adversarial Attack Generative Network—0
Adversarial Attacks for Multi-view Deep Models—0
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems—0
Semantic Autoencoder and Its Potential Usage for Adversarial Attack—0
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization—0
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples—0
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework—0
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification—0
Brightness-Restricted Adversarial Attack Patch—0
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools—0
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack—0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images—0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries—0
Adversarial Attacks and Dimensionality in Text Classifiers—0
CAAD 2018: Iterative Ensemble Adversarial 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