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 751–800 of 1808 papers

TitleStatusHype
A Formalization of Robustness for Deep Neural Networks—0
Adversarial Attacks on AI-Generated Text Detection Models: A Token Probability-Based Approach Using Embeddings—0
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers—0
Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems—0
Affine Disentangled GAN for Interpretable and Robust AV Perception—0
Chain Association-based Attacking and Shielding Natural Language Processing Systems—0
AEMIM: Adversarial Examples Meet Masked Image Modeling—0
Adversarial Attacks Neutralization via Data Set Randomization—0
Certifiably Robust Variational Autoencoders—0
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation—0
AdvSwap: Covert Adversarial Perturbation with High Frequency Info-swapping for Autonomous Driving Perception—0
Adversarial Attacks in Sound Event Classification—0
Capsule Neural Networks as Noise Stabilizer for Time Series Data—0
CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification—0
AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture—0
Can We Rely on AI?—0
Patch Synthesis for Property Repair of Deep Neural Networks—0
Adversarial Attacks in Multimodal Systems: A Practitioner's Survey—0
AdvCodeMix: Adversarial Attack on Code-Mixed Data—0
Can We Really Trust Explanations? Evaluating the Stability of Feature Attribution Explanation Methods via Adversarial Attack—0
Can the state of relevant neurons in a deep neural networks serve as indicators for detecting adversarial attacks?—0
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
CAG: A Real-time Low-cost Enhanced-robustness High-transferability Content-aware Adversarial Attack Generator—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
CAAD 2018: Iterative Ensemble Adversarial Attack—0
Adversarial Attacks for Multi-view Deep Models—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—0
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries—0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images—0
AdvHaze: Adversarial Haze Attack—0
BruSLeAttack: A Query-Efficient Score-Based Black-Box Sparse Adversarial Attack—0
Making Corgis Important for Honeycomb Classification: Adversarial Attacks on Concept-based Explainability Tools—0
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems—0
Brightness-Restricted Adversarial Attack Patch—0
Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework—0
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems—0
Adversarial Attacks and Dimensionality in Text Classifiers—0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
Unsourced Adversarial CAPTCHA: A Bi-Phase Adversarial CAPTCHA Framework—0
Bregman Linearized Augmented Lagrangian Method for Nonconvex Constrained Stochastic Zeroth-order Optimization—0
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium—0
Boosting Decision-Based Black-Box Adversarial Attack with Gradient Priors—0
Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy—0
Boosting Black-Box Adversarial Attacks with Meta Learning—0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs—0
Boosting Adversarial Transferability via High-Frequency Augmentation and Hierarchical-Gradient Fusion—0
Boosting Adversarial Transferability using Dynamic Cues—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