In this paper, we propose ES Attack, a novel model stealing attack without any data hurdles. By using heuristically generated synthetic data, ES Attack iteratively trains a substitute model and eventually achieves a functionally equivalent copy of the victim DNN.
Sep 21, 2020 · However, most existing works undervalued the impact of such attacks, where a successful attack has to acquire confidential training data or ...
The experimental results reveal the severity of ES Attack, a novel model stealing attack without any data hurdles that successfully steals the victim model ...
However, most existing works undervalued the impact of such attacks, where a successful attack has to acquire confidential training data or auxiliary data ...
ES Attack: Model Stealing Against Deep Neural Networks Without Data Hurdles. X. Yuan, L. Ding, L. Zhang, X. Li, and D. Wu. IEEE Trans. Emerg. Top. Comput.
Diagram of ES Attack. ... Figure 1: Diagram of ES Attack. Figure 3: Substitute model accuracy during attacks. Figure 4: The DNN generator G used in DNN-SYN.
ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles ... Attack; iii) ES Attack facilitates further attacks relying on the stolen model.
SoK: Model Inversion Attack Landscape: Taxonomy, Challenges, and Future Roadmap (Sayanton Dibbo, 2023) · A Survey of Privacy Attacks in Machine Learning (Rigaki ...
ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles ... Attack; iii) ES Attack facilitates further attacks relying on the stolen model.
Es attack: Model stealing against deep neural networks without data hurdles. IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 6, 5 ...