Adversarial Attack

Adversarial attacks aim to deceive machine learning models by subtly altering input data, causing misclassifications or other erroneous outputs. Current research focuses on developing more robust models and detection methods, exploring various attack strategies across different model architectures (including vision transformers, recurrent neural networks, and graph neural networks) and data types (images, text, signals, and tabular data). Understanding and mitigating these attacks is crucial for ensuring the reliability and security of AI systems in diverse applications, from autonomous vehicles to medical diagnosis and cybersecurity.

Papers

September 4, 2024

Adversarial Attacks on Machine Learning-Aided Visualizations
Takanori Fujiwara, Kostiantyn Kucher, Junpeng Wang, Rafael M. Martins, Andreas Kerren, Anders Ynnerman
Adversarial Attack New Machine Relevant Visualization Adversarial Machine Learning VIS4ML Research

September 3, 2024

September 2, 2024

September 1, 2024

Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers
Rahul Yumlembam, Biju Issac, Seibu Mary Jacob, Longzhi Yang
Adversarial Attack Adversarial Example Adversarial Sample Conformal FRP@TPR95 Metric Botnet Related Malware BotNet Detection

August 30, 2024

August 29, 2024

STEREO: Towards Adversarially Robust Concept Erasing from Text-to-Image Generation Models
Koushik Srivatsan, Fahad Shamshad, Muzammal Naseer, Karthik Nandakumar
Adversarial Attack Adversarial Training Adversarial Prompt Text to Image Generation Model Solar Dynamic Observatory Large Scale Text to Image

August 28, 2024

August 26, 2024

August 24, 2024

Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach
Jiwei Guan, Tianyu Ding, Longbing Cao, Lei Pan, Chen Wang, Xi Zheng
Adversarial Attack Native Robustness Adversarial Robustness Vision Language Adversarial Perturbation Adversarial Vulnerability Multimodal Attack

August 23, 2024

Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks
Zhenyu Liu, Haoran Duan, Huizhi Liang, Yang Long, Vaclav Snasel, Guiseppe Nicosia, Rajiv Ranjan, Varun Ojha
Adversarial Attack Adversarial Example Adversarial Training Model Robustness Neural Network Robustness Adversarial Distillation Adversarial Discriminator

August 22, 2024

BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks
Woojin Shin, Donghwa Kang, Daejin Choi, Brent Kang, Jinkyu Lee, Hyeongboo Baek
Adversarial Attack Multi Object Tracking Multiple Object Tracking Tracking by Detection Attribute Manipulation

Adversarial Attack

Papers

Adversarial Attacks on Machine Learning-Aided Visualizations

Reassessing Noise Augmentation Methods in the Context of Adversarial Speech

Purification-Agnostic Proxy Learning for Agentic Copyright Watermarking against Adversarial Evidence Forgery

One-Index Vector Quantization Based Adversarial Attack on Image Classification

A Review of Image Retrieval Techniques: Data Augmentation and Adversarial Learning Approaches

Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers

Exact Recovery Guarantees for Parameterized Non-linear System Identification Problem under Adversarial Attacks

Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning

STEREO: Towards Adversarially Robust Concept Erasing from Text-to-Image Generation Models

Multi-modal Adversarial Training for Zero-Shot Voice Cloning

Evaluating Model Robustness Using Adaptive Sparse L0 Regularization

Certified Causal Defense with Generalizable Robustness

Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting for Smart and Connected Infrastructures

Feedback-based Modal Mutual Search for Attacking Vision-Language Pre-training Models

TART: Boosting Clean Accuracy Through Tangent Direction Guided Adversarial Training

2D-Malafide: Adversarial Attacks Against Face Deepfake Detection Systems

TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models

Probing the Robustness of Vision-Language Pretrained Models: A Multimodal Adversarial Attack Approach

Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks

BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Banks