Adversarial Example

Adversarial examples are subtly altered inputs designed to fool machine learning models, primarily deep neural networks (DNNs), into making incorrect predictions. Current research focuses on improving model robustness against these attacks, exploring techniques like ensemble methods, multi-objective representation learning, and adversarial training, often applied to architectures such as ResNets and Vision Transformers. Understanding and mitigating the threat of adversarial examples is crucial for ensuring the reliability and security of AI systems across diverse applications, from image classification and natural language processing to malware detection and autonomous driving. The development of robust defenses and effective attack detection methods remains a significant area of ongoing investigation.

Papers

February 18, 2022

February 17, 2022

Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition
Chao-Han Huck Yang, Zeeshan Ahmed, Yile Gu, Joseph Szurley, Roger Ren, Linda Liu, Andreas Stolcke, Ivan Bulyko
Adversarial Example End to End Adversarial Noise Textual Adversarial Example Bayesian Neural

February 16, 2022

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining
Murat Kuzlu, Ferhat Ozgur Catak, Umit Cali, Evren Catak, Ozgur Guler
Deep Neural Network Adversarial Attack Adversarial Example mmWave Signal Prediction Model Beam Prediction Adversarial Retraining Defensive Distillation

February 14, 2022

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark
Yonghao Xu, Pedram Ghamisi
Adversarial Attack New Benchmark Adversarial Example NCD Method Remote Sensing

February 12, 2022

RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation
Zhen Li, Guenevere, Chen, Chen Chen, Yayi Zou, Shouhuai Xu
Adversarial Example Code Style Transfer Code Authorship Attribution

February 11, 2022

February 9, 2022

February 7, 2022

On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks
Salijona Dyrmishi, Salah Ghamizi, Thibault Simonetto, Yves Le Traon, Maxime Cordy
Adversarial Example Adversarial Robustness Real World Attack Realistic Adversarial

February 6, 2022

Tubes Among Us: Analog Attack on Automatic Speaker Identification
Shimaa Ahmed, Yash Wani, Ali Shahin Shamsabadi, Mohammad Yaghini, Ilia Shumailov, Nicolas Papernot, Kassem Fawaz
Machine Learning Adversarial Example Speaker Identification Voice Authentication Draft Tube

February 5, 2022

Adversarial Detector with Robust Classifier
Takayuki Osakabe, Maungmaung Aprilpyone, Sayaka Shiota, Hitoshi Kiya
Adversarial Example Adversarial Detection Robust Classifier

February 1, 2022

January 28, 2022

Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning
Jie Zhang, Lei Zhang, Gang Li, Chao Wu
Machine Learning Model Adversarial Example Imbalanced Learning Biased Decision First Benchmark Dataset