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
Test-time adversarial detection and robustness for localizing humans using ultra wide band channel impulse responses
Abhiram Kolli, Muhammad Jehanzeb Mirza, Horst Possegger, Horst Bischof
Impact of Adversarial Training on Robustness and Generalizability of Language Models
Enes Altinisik, Hassan Sajjad, Husrev Taha Sencar, Safa Messaoud, Sanjay Chawla
Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples
Meng Chen, Li Lu, Jiadi Yu, Yingying Chen, Zhongjie Ba, Feng Lin, Kui Ren
Improving transferability of 3D adversarial attacks with scale and shear transformations
Jinali Zhang, Yinpeng Dong, Jun Zhu, Jihong Zhu, Minchi Kuang, Xiaming Yuan
Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise
Jhih-Cing Huang, Yu-Lin Tsai, Chao-Han Huck Yang, Cheng-Fang Su, Chia-Mu Yu, Pin-Yu Chen, Sy-Yen Kuo
LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification
Xing Chen, Jie Wang, Xiao-Lei Zhang, Wei-Qiang Zhang, Kunde Yang
The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Training
Junhao Dong, Seyed-Mohsen Moosavi-Dezfooli, Jianhuang Lai, Xiaohua Xie
DensePure: Understanding Diffusion Models towards Adversarial Robustness
Chaowei Xiao, Zhongzhu Chen, Kun Jin, Jiongxiao Wang, Weili Nie, Mingyan Liu, Anima Anandkumar, Bo Li, Dawn Song
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Jianan Zhou, Jianing Zhu, Jingfeng Zhang, Tongliang Liu, Gang Niu, Bo Han, Masashi Sugiyama
ARDIR: Improving Robustness using Knowledge Distillation of Internal Representation
Tomokatsu Takahashi, Masanori Yamada, Yuuki Yamanaka, Tomoya Yamashita