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 801–850 of 1808 papers

TitleStatusHype
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models—0
Adversarial Attack with Raindrops—0
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System—0
Identification of Systematic Errors of Image Classifiers on Rare Subgroups—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs—0
Fooling Adversarial Training with Inducing Noise—0
Fooling Adversarial Training with Induction Noise—0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation—0
Defense of Adversarial Ranking Attack in Text Retrieval: Benchmark and Baseline via Detection—0
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution—0
Forbidden Facts: An Investigation of Competing Objectives in Llama-2—0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Meme Stock Prediction—0
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense—0
Defense-guided Transferable Adversarial Attacks—0
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks—0
Adversarial Attack with Pattern Replacement—0
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models—0
Defense Against Explanation Manipulation—0
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection—0
Frequency-Tuned Universal Adversarial Attacks—0
Analysis of the vulnerability of machine learning regression models to adversarial attacks using data from 5G wireless networks—0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks—0
An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks—0
From Sound Representation to Model Robustness—0
Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning—0
GAIM: Attacking Graph Neural Networks via Adversarial Influence Maximization—0
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks—0
An Adversarial Attack via Feature Contributive Regions—0
How Sampling Impacts the Robustness of Stochastic Neural Networks—0
An Adversarial Attack Defending System for Securing In-Vehicle Networks—0
Post-train Black-box Defense via Bayesian Boundary Correction—0
Adversarial Attack Type I: Cheat Classifiers by Significant Changes—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case—0
Generating Adversarial Examples with an Optimized Quality—0
Generating Adversarial Inputs Using A Black-box Differential Technique—0
Generating Black-Box Adversarial Examples in Sparse Domain—0
Benchmarking Adversarial Robustness—0
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline—0
Generating Out of Distribution Adversarial Attack using Latent Space Poisoning—0
Generating Semantic Adversarial Examples via Feature Manipulation—0
Defending Against Adversarial Examples by Regularized Deep Embedding—0
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training—0
I2VGuard: Safeguarding Images against Misuse in Diffusion-based Image-to-Video Models—0
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training—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