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 1–50 of 1808 papers

TitleStatusHype
3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving—0
VIP: Visual Information Protection through Adversarial Attacks on Vision-Language ModelsCode0
Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF InfeasibleCode0
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion ModelsCode1
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage GenerationCode0
Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttackCode0
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis—0
DRO-Augment Framework: Robustness by Synergizing Wasserstein Distributionally Robust Optimization and Data Augmentation—0
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
Doppelganger Method: Breaking Role Consistency in LLM Agent via Prompt-based Transferable Adversarial Attack—0
Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories—0
Alphabet Index Mapping: Jailbreaking LLMs through Semantic Dissimilarity—0
Second Order State Hallucinations for Adversarial Attack Mitigation in Formation Control of Multi-Agent Systems—0
On the existence of consistent adversarial attacks in high-dimensional linear classification—0
Unsourced Adversarial CAPTCHA: A Bi-Phase Adversarial CAPTCHA Framework—0
Boosting Adversarial Transferability for Hyperspectral Image Classification Using 3D Structure-invariant Transformation and Intermediate Feature Distance—0
A look at adversarial attacks on radio waveforms from discrete latent space—0
AdversariaL attacK sAfety aLIgnment(ALKALI): Safeguarding LLMs through GRACE: Geometric Representation-Aware Contrastive Enhancement- Introducing Adversarial Vulnerability Quality Index (AVQI)—0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model ReliabilityCode0
Efficient Robust Conformal Prediction via Lipschitz-Bounded NetworksCode0
CAPAA: Classifier-Agnostic Projector-Based Adversarial AttackCode0
Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems—0
3D Gaussian Splat VulnerabilitiesCode1
Learning Safety Constraints for Large Language ModelsCode1
SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM AgentsCode1
Adversarial Semantic and Label Perturbation Attack for Pedestrian Attribute RecognitionCode0
Seeing the Threat: Vulnerabilities in Vision-Language Models to Adversarial Attack—0
TabAttackBench: A Benchmark for Adversarial Attacks on Tabular DataCode0
Adversarial Attacks against Closed-Source MLLMs via Feature Optimal AlignmentCode2
A Framework for Adversarial Analysis of Decision Support Systems Prior to Deployment—0
Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion—0
TESSER: Transfer-Enhancing Adversarial Attacks from Vision Transformers via Spectral and Semantic Regularization—0
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation—0
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box FrameworkCode1
Ownership Verification of DNN Models Using White-Box Adversarial Attacks with Specified Probability Manipulation—0
Temporal Consistency Constrained Transferable Adversarial Attacks with Background Mixup for Action RecognitionCode0
Towards more transferable adversarial attack in black-box manner—0
Tropical Attention: Neural Algorithmic Reasoning for Combinatorial Algorithms—0
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems—0
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium—0
EVALOOP: Assessing LLM Robustness in Programming from a Self-consistency Perspective—0
FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models—0
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Adversarial Attack on Large Language Models using Exponentiated Gradient DescentCode0
Evaluating the Robustness of Adversarial Defenses in Malware Detection SystemsCode0
Towards Adaptive Meta-Gradient Adversarial Examples for Visual TrackingCode0
No Query, No Access—0
Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain—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