Biblio

With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms. To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts. Based on our observations, we propose Fake Generated Painting Detection via Frequency Analysis (FGPD-FA) by extracting three types of features in frequency domain. Besides, we also propose a digital fake painting detection database for assessing the proposed method. Experimental results demonstrate the excellence of the proposed method in different testing conditions.

The rapid growth of power Internet of Things devices has led to traditional data security sharing mechanisms that are no longer suitable for attribute and permission management of massive devices. In response to this problem, this article proposes a blockchain-based data security sharing mechanism for the power Internet of Things, which reduces the risk of data leakage through decentralization in the architecture and promotes the integration of multiple information and methods.