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

May 6, 2024

Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial Transferability
Juanjuan Weng, Zhiming Luo, Shaozi Li
Adversarial Attack Adversarial Example Adversarial Sample Frequency Feature Dominant Low Frequency Adversarial Feature Feature Mixing

May 3, 2024

A Novel Approach to Guard from Adversarial Attacks using Stable Diffusion
Trinath Sai Subhash Reddy Pittala, Uma Maheswara Rao Meleti, Geethakrishna Puligundla
Adversarial Attack Adversarial Example Novel Approach Stable Diffusion Robust Defense Safe AI Air Guardian System Adversarial Threat

April 30, 2024

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
Antonio Emanuele Cinà, Jérôme Rony, Maura Pintor, Luca Demetrio, Ambra Demontis, Battista Biggio, Ismail Ben Ayed, Fabio Roli
Adversarial Example New Attack Gradient Based Attack Novel Attack

April 29, 2024

A Systematic Evaluation of Adversarial Attacks against Speech Emotion Recognition Models
Nicolas Facchinetti, Federico Simonetta, Stavros Ntalampiras
Adversarial Attack Adversarial Example Long Short Term Memory Speech Emotion Recognition

April 27, 2024

April 24, 2024

April 21, 2024

Fermi-Bose Machine achieves both generalization and adversarial robustness
Mingshan Xie, Yuchen Wang, Haiping Huang
Adversarial Example New Machine Back Propagation Adversarial Vulnerability Hidden Layer Local Contrastive

April 20, 2024

Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think
Haotian Xue, Yongxin Chen
Diffusion Model Adversarial Attack Adversarial Example Adversarial Perturbation Adversarial Sample Tetromino Pixel Knowledge Barrier Adversarial Pattern

April 19, 2024

April 18, 2024

Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement
Pushkar Shukla, Dhruv Srikanth, Lee Cohen, Matthew Turk
Generative Model Fine Grained Adversarial Example Bias Mitigation Adversarial Image Accuracy Improvement Counterfactual Generation

April 16, 2024

April 15, 2024

Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models
Peifei Zhu, Tsubasa Takahashi, Hirokatsu Kataoka
Diffusion Model Adversarial Example Conditional Generative Adversarial Network Adversarial Loss Copyright Protection Visible Watermark

April 14, 2024

Counteracting Concept Drift by Learning with Future Malware Predictions
Branislav Bosansky, Lada Hospodkova, Michal Najman, Maria Rigaki, Elnaz Babayeva, Viliam Lisy
LeArning Abstract Generative Adversarial Network Adversarial Example Adversarial Training Concept Drift Based Malware

April 11, 2024

April 10, 2024

Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data
Yu Qiao, Chaoning Zhang, Apurba Adhikary, Choong Seon Hong
Adversarial Attack Adversarial Example Adversarial Training Raw Data Adversarial Robustness Logit Calibration

Adversarial Example

Papers

Exploring Frequencies via Feature Mixing and Meta-Learning for Improving Adversarial Transferability

A Novel Approach to Guard from Adversarial Attacks using Stable Diffusion

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples

A Systematic Evaluation of Adversarial Attacks against Speech Emotion Recognition Models

Adversarial Examples: Generation Proposal in the Context of Facial Recognition Systems

Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks

Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attacks

An Empirical Study of Aegis

Fermi-Bose Machine achieves both generalization and adversarial robustness

Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think

How Real Is Real? A Human Evaluation Framework for Unrestricted Adversarial Examples

AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation

Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement

Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models

Towards a Novel Perspective on Adversarial Examples Driven by Frequency

Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models

Counteracting Concept Drift by Learning with Future Malware Predictions

Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization

Persistent Classification: A New Approach to Stability of Data and Adversarial Examples

Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data