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Adversarial Attacks and Defences Competition

2018-03-31 · Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe

To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.

📄 PDF Abstract BibTeX arXiv:1804.00097

Code (1)

pfnet-research/nips17-adversarial-attack 공식 구현 tf

Tasks

BIG-bench Machine Learning

Adversarial purification for no-reference image-quality metrics: applicability study and new methods

2024-04-10 · Aleksandr Gushchin, Anna Chistyakova, Vladislav Minashkin, Anastasia Antsiferova 외

Recently, the area of adversarial attacks on image quality metrics has begun to be explored, whereas the area of defences remains under-researched. In this study, we aim to cover that case and check the transferability o…

Adversarial Purification Denoising

Preventing Adversarial AI Attacks Against Autonomous Situational Awareness: A Maritime Case Study

2025-05-27 · Mathew J. Walter, Aaron Barrett, Kimberly Tam

Adversarial artificial intelligence (AI) attacks pose a significant threat to autonomous transportation, such as maritime vessels, that rely on AI components. Malicious actors can exploit these systems to deceive and man…

SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

2023-01-11 · Maxwell Standen, Junae Kim, Claudia Szabo

Multi-Agent Reinforcement Learning (MARL) is vulnerable to Adversarial Machine Learning (AML) attacks and needs adequate defences before it can be used in real world applications. We have conducted a survey into the use …

Deep Reinforcement Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey

2022-06-16 · Abhijith Sharma, Yijun Bian, Phil Munz, Apurva Narayan

Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security and robustness of AI models. Yet the more…

Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs

2025-02-21 · Giulio Zizzo, Giandomenico Cornacchia, Kieran Fraser, Muhammad Zaid Hameed 외

As large language models (LLMs) become integrated into everyday applications, ensuring their robustness and security is increasingly critical. In particular, LLMs can be manipulated into unsafe behaviour by prompts known…

Benchmarking