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 551–600 of 1808 papers

TitleStatusHype
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks—0
OTAD: An Optimal Transport-Induced Robust Model for Agnostic Adversarial Attack—0
Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks—0
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Cross-Task Attack: A Self-Supervision Generative Framework Based on Attention Shift—0
Compressed models are NOT miniature versions of large models—0
Any Target Can be Offense: Adversarial Example Generation via Generalized Latent InfectionCode0
AEMIM: Adversarial Examples Meet Masked Image Modeling—0
Enhancing TinyML Security: Study of Adversarial Attack Transferability—0
Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical AnalysisCode0
Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks—0
Transferable 3D Adversarial Shape Completion using Diffusion ModelsCode0
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images—0
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition SystemsCode0
DLOVE: A new Security Evaluation Tool for Deep Learning Based Watermarking Techniques—0
Rethinking Targeted Adversarial Attacks For Neural Machine TranslationCode0
Self-Supervised Representation Learning for Adversarial Attack Detection—0
TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer TrackersCode0
JailbreakHunter: A Visual Analytics Approach for Jailbreak Prompts Discovery from Large-Scale Human-LLM Conversational Datasets—0
L_p-norm Distortion-Efficient Adversarial Attack—0
Looking From the Future: Multi-order Iterations Can Enhance Adversarial Attack Transferability—0
EvolBA: Evolutionary Boundary Attack under Hard-label Black Box condition—0
Query-Efficient Hard-Label Black-Box Attack against Vision Transformers—0
Emotion Loss Attacking: Adversarial Attack Perception for Skeleton based on Multi-dimensional Features—0
IDT: Dual-Task Adversarial Attacks for Privacy Protection—0
Deceptive Diffusion: Generating Synthetic Adversarial Examples—0
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification—0
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI—0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning—0
AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization—0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation—0
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor AttacksCode0
Imperceptible Face Forgery Attack via Adversarial Semantic MaskCode0
KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge GraphsCode0
Explainable Graph Neural Networks Under FireCode0
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks—0
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner—0
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise—0
Graph Neural Network Explanations are FragileCode0
SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural NetworksCode0
Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function PriorCode0
Wavelet-Based Image Tokenizer for Vision Transformers—0
Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets—0
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
Rethinking Independent Cross-Entropy Loss For Graph-Structured DataCode0
Adversarial Attacks on Hidden Tasks in Multi-Task Learning—0
AdjointDEIS: Efficient Gradients for Diffusion ModelsCode0
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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