Abstract: A method for regulating an unmanned aerial vehicle (UAV) includes receiving a UAV identifier and one or more types of contextual information broadcasted by the UAV. The UAV identifier uniquely identifies the UAV from other UAVs. The one or more types of contextual information includes at least geographical information of the UAV. The method further includes authenticating, via an authentication device, an identity of the UAV based on the UAV identifier to determine whether the UAV is authorized for operation, and transmitting a signal to a remote device in response to determining whether the UAV is authorized for operation.

Abstract: An image privacy perception method based on deep learning, including the following steps: S1, building a privacy classification data set with labeled categories, and training a privacy perception network with a transfer learning method; S2, recognizing a privacy image using a deep convolutional neural network oriented to privacy perception; and S3, extracting an attention profile according to deep convolutional features of the neural network, and locating an attention focusing region to complete the perception of an image privacy region. The method has the following beneficial effects: by completing end-to-end training and testing based on the deep neural network, the privacy image can be accurately distinguished with the privacy region located, facilitating the selective protection of the privacy information in the image.

Abstract: An image privacy perception method based on deep learning, including the following steps: S1, building a privacy classification data set with labeled categories, and training a privacy perception network with a transfer learning method; S2, recognizing a privacy image using a deep convolutional neural network oriented to privacy perception; and S3, extracting an attention profile according to deep convolutional features of the neural network, and locating an attention focusing region to complete the perception of an image privacy region. The method has the following beneficial effects: by completing end-to-end training and testing based on the deep neural network, the privacy image can be accurately distinguished with the privacy region located, facilitating the selective protection of the privacy information in the image.