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
Exploiting the Layered Intrinsic Dimensionality of Deep Models for Practical Adversarial Training
Enes Altinisik, Safa Messaoud, Husrev Taha Sencar, Hassan Sajjad, Sanjay Chawla
Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models
Fengfan Zhou, Qianyu Zhou, Xiangtai Li, Xuequan Lu, Lizhuang Ma, Hefei Ling
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective
Nils Philipp Walter, Linara Adilova, Jilles Vreeken, Michael Kamp
Generating camera failures as a class of physics-based adversarial examples
Manav Prabhakar, Jwalandhar Girnar, Arpan Kusari
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection
Marco Rando, Luca Demetrio, Lorenzo Rosasco, Fabio Roli
Learning to Transform Dynamically for Better Adversarial Transferability
Rongyi Zhu, Zeliang Zhang, Susan Liang, Zhuo Liu, Chenliang Xu
On Adversarial Examples for Text Classification by Perturbing Latent Representations
Korn Sooksatra, Bikram Khanal, Pablo Rivas
Is ReLU Adversarially Robust?
Korn Sooksatra, Greg Hamerly, Pablo Rivas
Cutting through buggy adversarial example defenses: fixing 1 line of code breaks Sabre
Nicholas Carlini
Competitive strategies to use "warm start" algorithms with predictions
Vaidehi Srinivas, Avrim Blum