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 1451–1500 of 1808 papers

TitleStatusHype
Adversarial Attacks Neutralization via Data Set Randomization—0
Understanding Oversmoothing in GNNs as Consensus in Opinion Dynamics—0
Understanding Pose and Appearance Disentanglement in 3D Human Pose Estimation—0
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks—0
Search Space of Adversarial Perturbations against Image Filters—0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks—0
Second-Order Adversarial Attack and Certifiable Robustness—0
A Brief Survey on Deep Learning Based Data Hiding—0
Second-Order NLP Adversarial Examples—0
Second Order State Hallucinations for Adversarial Attack Mitigation in Formation Control of Multi-Agent Systems—0
Secure Diagnostics: Adversarial Robustness Meets Clinical Interpretability—0
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples—0
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
AVTrustBench: Assessing and Enhancing Reliability and Robustness in Audio-Visual LLMs—0
A White-Box Adversarial Attack Against a Digital Twin—0
Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack—0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction—0
Automated Trustworthiness Oracle Generation for Machine Learning Text Classifiers—0
Automated Decision-based Adversarial Attacks—0
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing—0
AutoAugment Input Transformation for Highly Transferable Targeted Attacks—0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack—0
Augmented Adversarial Trigger Learning—0
Bandlimiting Neural Networks Against Adversarial Attacks—0
BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks—0
Aug-ILA: More Transferable Intermediate Level Attacks with Augmented References—0
CE-based white-box adversarial attacks will not work using super-fitting—0
Audio Adversarial Examples: Attacks Using Vocal Masks—0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
Seeing is Deceiving: Exploitation of Visual Pathways in Multi-Modal Language Models—0
Benchmarking Adversarially Robust Quantum Machine Learning at Scale—0
Benchmarking Adversarial Robustness—0
Benchmarking Adversarial Robustness of Image Shadow Removal with Shadow-adaptive Attacks—0
Attribution for Enhanced Explanation with Transferable Adversarial eXploration—0
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection—0
Benign Adversarial Attack: Tricking Models for Goodness—0
Attribution-driven Causal Analysis for Detection of Adversarial Examples—0
Seeing the Threat: Vulnerabilities in Vision-Language Models to Adversarial Attack—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Attribute-Guided Encryption with Facial Texture Masking—0
Seeking Flat Minima over Diverse Surrogates for Improved Adversarial Transferability: A Theoretical Framework and Algorithmic Instantiation—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation—0
Adversarial Attacks in Sound Event Classification—0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
Self adversarial attack as an augmentation method for immunohistochemical stainings—0
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing—0
Bias Field Poses a Threat to DNN-based X-Ray Recognition—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