一、模型逆向攻击(Model Inversion Attack, MIA)的意义 二、模型逆向攻击的本质与原理 三、模型逆向攻击的问题与发展 1、目标模型鲁棒性以及泛化能力的影响 2、局部最小问题以及相关研究进展 3、黑盒攻击相关研究进展与问题 四、总结 参考文献 这是我个人对该领域的理解并从意义、本质和原理、问题与发展三个方面浅...
Leng, J., et al.: An inference attack model for flow table capacity and usage: Exploiting the vulnerability of flow table overflow in software-defined network. arXiv preprint arXiv:1504.03095 (2015)Leng, Junyuan et al.: An inference attack model for flow table capacity and usage: Exploiting...
已有的攻击方法从未考虑过对生成的图片进行筛选,P&P使用membership inference attack的思想,筛选出尽量与training data(也就是隐私数据)相似的生成图片。具体的筛选方法是对生成的图片进行随机的转换,然后再放入target model中,选出预测分数最高的50个作为最终的攻击结果。这个筛选方法的思想是:较差的生成图片会过拟合于...
[TIFS 2022] (white-box) Model Inversion Attack by Integration of Deep Generative Models: Privacy-Sensitive Face Generation From a Face Recognition System [paper] [TIFS 2022] (black-box (defense)) One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy [paper] [WAC...
Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet companies to deploy machine learning as a service (MLaaS). Recently, the first membership inference attack has show...
inference, the model itself and the inference-generated intermediate data and output files are either in the host’s memory or file system; malicious software also could attack through technologies such as memory snooping. Further, to reduce the attack surface and protect from privileged processes ...
论文阅读 | NDSS19 | ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on ML,程序员大本营,技术文章内容聚合第一站。
•High latency:The response time for LLM inference is significantly longer than that of other applications, making AI applications vulnerable to attacks, such as sustained slow request flooding. This type of attack is easy to carry out but can introduce substantial overhead on the server. ...
发布在ICDE2021的一篇文章,链接:Feature Inference Attack on Model Predictions in Vertical Federated Learning 相关阅读:联邦学习安全么?(数据攻击) 本篇文章名为纵向联邦预测模型的特征推理攻击,本文贡献为: 在我们通常的纵向联邦学习中,把多方相互之间的特征值通过一些各自的处理,在相互之间传一些参数来实现共同模型的...
C.A. Choquette-Choo et al. Label-only membership inference attacksView more references Cited by (17) A realistic model extraction attack against graph neural networks 2024, Knowledge-Based Systems Citation Excerpt : They examined four key contributors to a successful attack: the target model’s ta...