Enhancement Attack

Enhancement attacks manipulate data or models to artificially inflate performance metrics, undermining the trustworthiness of machine learning systems. Current research focuses on developing these attacks across various domains, including image recognition, text-to-image generation, and biomedical applications, exploring techniques like data augmentation manipulation and decision path coupling to achieve subtle yet impactful improvements. The ability to create such attacks highlights critical vulnerabilities in model evaluation and raises significant concerns about the reliability of results in high-stakes applications, demanding improved robustness checks and data provenance tracking.

Papers

March 5, 2024

A general approach to enhance the survivability of backdoor attacks by decision path coupling
Yufei Zhao, Dingji Wang, Bihuan Chen, Ziqian Chen, Xin Peng
Backdoor Attack General Framework Backdoor Defense Decision Path Enhancement Attack

November 29, 2023

VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models
Xiang Li, Qianli Shen, Kenji Kawaguchi
Generative Model Copyright Protection Text to Image Generative Model Attack Framework Enhancement Attack

September 18, 2023

Spoofing attack augmentation: can differently-trained attack models improve generalisation?
Wanying Ge, Xin Wang, Junichi Yamagishi, Massimiliano Todisco, Nicholas Evans
Strong Generalization Deepfake Detection Attack Algorithm Enhancement Attack

June 13, 2023

Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats
Gaolei Li, Yuanyuan Zhao, Wenqi Wei, Yuchen Liu
Threat Intelligence Cross Domain Few Shot Learning Advanced Persistent Threat APT Detection Enhancement Attack

May 22, 2023

Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
Simin Li, Shuing Zhang, Gujun Chen, Dong Wang, Pu Feng, Jiakai Wang, Aishan Liu, Xin Yi, Xianglong Liu
Adversarial Attack Benchmark Platform Visual Naturalness Physical World Attack Naturalness Assessment Benchmark Attack Enhancement Attack

April 8, 2023

Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning
Binqian Xu, Xiangbo Shu, Jiachao Zhang, Rui Yan, Guo-Sen Xie
Contrastive Learning Skeleton Based Action Augmentation Composition Enhancement Attack Prototype Contrastive Learning

January 5, 2023

Enhancement attacks in biomedical machine learning
Matthew Rosenblatt, Javid Dadashkarimi, Dustin Scheinost
Machine Learning Adversarial Attack Better Data Enhancement Attack

September 29, 2022

Augmentation Backdoors
Joseph Rance, Yiren Zhao, Ilia Shumailov, Robert Mullins
Data Augmentation Backdoor Attack Augmentation Method Backdoor Injection Enhancement Attack

Enhancement Attack

Papers

A general approach to enhance the survivability of backdoor attacks by decision path coupling

VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models

Spoofing attack augmentation: can differently-trained attack models improve generalisation?

Few-shot Multi-domain Knowledge Rearming for Context-aware Defence against Advanced Persistent Threats

Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks

Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning

Enhancement attacks in biomedical machine learning

Augmentation Backdoors