Zhang等人 [5] 首个提出生成式模型逆向攻击(Generative Model Inversion Attack, GMI),它可以反转 DNN 并高保真地合成私有训练数据,通过辅助数据作为先验知识进行约束解决原本无约束的逆向问题。Struppek等人 [10] 证明由于梯度消失,交叉熵损失不是最佳的损失函数,并且为了解决局部最小问题提出采用庞加莱距离代替,扩大优...
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...
However, in reality, it will be inefficient to transfer huge datasets to online services to get prediction values of inference models. More seriously, the huge transmission may cause the administrator's active defense. In this paper, we propose a novel MIA scheme which reduce queries on ...
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...
Recently, the first membership inference attack has shown that extraction of information on the training set is possible in such MLaaS settings, which has severe security and privacy implications. However, the early demonstrations of the feasibility of such attacks have many assumptions on the ...
Model inversion attackis an emerging powerful private data theft attack, where a malicious attacker is able to reconstruct data with the same distribution as the training dataset of the target model. The reason why we developed this toolbox is that the research line ofMIsuffers from a lack of...
This paper proposes a novel auxiliary model-based label inference attack framework against learning, named SplitAUM. SplitAUM first builds an auxiliary model on the client side using intermediate representations of the cut layer and a small number of dummy labels. Then, the learning regularization ...
Zhang S, Song S (2011) A novel attack graph posterior inference model based on Bayesian network. J Inf Secur 2:8-27Zhang, S., & Song, S. (2011). A novel attack graph posterior inference model based on Bayesian network. Journal of Information Security, 2(01), 8....
For Natural langauge inference: python attack_nli.py Examples of run code for these two files are inrun_attack_classification.pyandrun_attack_nli.py. Here we explain each required argument in details: --dataset_path: The path to the dataset. We put the 1000 examples for each dataset we ...
(FC) layer. We show that this results in a resource overhead which grows larger with an increasing number of clients. We show that this resource overhead is caused by an incorrect perspective in all prior work that treats an attack on an aggregate update in the same way as an individual...