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The lack of remote sensing images and poor quality limit the performance improvement of follow-up research such as remote sensing interpretation. In this article, a generative adversarial network (GAN) is proposed for data augmentation of remote sensing images abstracted from Jiangxi and Anhui Provinces in China, i.e., deeply supervised GAN (D-sGAN). D-sGAN can generate high-quality images that are rich in changes, greatly shorten the generation time, and provide data support for applications such as semantic interpretation of remote sensing images. First, to modulate the layer activations, a downsampling scheme is designed based on the segmentation map. Then, the architecture of the generator is Unet++ with the proposed downsampling module. Next, the generator of this net is deeply supervised by the discriminator using deep convolutional neural network. This article further proved that the proposed downsampling module and the dense connection characteristics of UNet++ are significantly beneficial to the retention of semantic information of remote sensing images. Numerical results demonstrated that the images generated by D-sGAN could be used to improve accuracy of the segmentation network, with the faster generation speed compared to the CoGAN, SimGAN, and CycleGAN models. Furthermore, the remote sensing data generated by the model helped the interpretation network to increase the accuracy by 9%, meeting actual generation requirements.

The classification of benign and malignant parotid tumors is very crucial for the selection of surgical methods and their prognoses. The wide application of deep learning technology in the field of medical imaging also provides new ideas for the computer-aided diagnosis of parotid gland tumors. In addition, because the pathological types of parotid gland tumors are very complicated and the computed tomography (CT) images of benign and malignant patients are also very similar, some clinicians may misjudge tumors due to a lack of experience, which affects the effect of surgical treatment and prognosis. Therefore, this research proposes using deep learning methods to solve this problem. This study uses the four classic pretraining models of VGG16, InceptionV3, ResNet and DenseNet to classify parotid CT images using transfer learning methods and uses an improved convolutional neural network (CNN) model to classify parotid CT images. The experimental results show that the improved CNN model achieves an accuracy of 97.78%, and its classification performance is better than those of the other four transfer learning methods. It can effectively diagnose benign and malignant parotid tumors and improve the diagnostic accuracy.

Nowadays, autonomous driving has attracted much attention from both industry and academia. Convolutional neural network (CNN) is a key component in autonomous driving, which is also increasingly adopted in pervasive computing such as smartphones, wearable devices, and IoT networks. Prior work shows CNN-based classification models are vulnerable to adversarial attacks. However, it is uncertain to what extent regression models such as driving models are vulnerable to adversarial attacks, the effectiveness of existing defense techniques, and the defense implications for system and middleware builders. This paper presents an in-depth analysis of five adversarial attacks and four defense methods on three driving models. Experiments show that, similar to classification models, these models are still highly vulnerable to adversarial attacks. This poses a big security threat to autonomous driving and thus should be taken into account in practice. While these defense methods can effectively defend against different attacks, none of them are able to provide adequate protection against all five attacks. We derive several implications for system and middleware builders: (1) when adding a defense component against adversarial attacks, it is important to deploy multiple defense methods in tandem to achieve a good coverage of various attacks, (2) a blackbox attack is much less effective compared with a white-box attack, implying that it is important to keep model details (e.g., model architecture, hyperparameters) confidential via model obfuscation, and (3) driving models with a complex architecture are preferred if computing resources permit as they are more resilient to adversarial attacks than simple models.

Steerable properties dominate the design of traditional filters, e.g., Gabor filters, and endow features the capability of dealing with spatial transformations. However, such excellent properties have not been well explored in the popular deep convolutional neural networks (DCNNs). In this paper, we propose a new deep model, termed Gabor Convolutional Networks (GCNs or Gabor CNNs), which incorporates Gabor filters into DCNNs to enhance the resistance of deep learned features to the orientation and scale changes. By only manipulating the basic element of DCNNs based on Gabor filters, i.e., the convolution operator, GCNs can be easily implemented and are compatible with any popular deep learning architecture. Experimental results demonstrate the super capability of our algorithm in recognizing objects, where the scale and rotation changes occur frequently. The proposed GCNs have much fewer learnable network parameters, and thus is easier to train with an endtoend pipeline. The source code will be here 1.