The security of wireless challenge-response Physical Layer Authentication (PLA) based on Orthogonal Frequency Division Multiplexing (OFDM) relies on a sufficiently random fading channel condition, which is commonly assumed in existing studies. However, in practical scenarios, such a condition is not always guaranteed and the responses of OFDM subchannels may exhibit correlation. Consequently, ensuring the security of such PLA systems remains an unsolved problem. In this paper, we propose a novel adversary model, called Maximum Differential Likelihood Generator (MDLG), which exploits the weak correlation property in practical wireless channel to launch effective attacks against PLA. Based on this model, we create a measurable guideline using randomness testing to decide when we can in fact use PLA in a practical wireless channel condition. Extensive real-world experiments validate the effectiveness of the MDLG attack and demonstrate how the proposed guideline can help protect the security of PLA.

Convolution neural networks (CNNs) are extensively utilized in hyperspectral image (HSI) classification due to their remarkable capability to extract features from patterns with fixed shapes. These networks have been shown to effectively capture features at the pixel level. However, the fixed shape of convolution kernels poses a challenge for CNNs to adapt to the diverse shapes found in HSIs. Graph neural networks (GNNs), particularly graph convolution networks (GCNs), possess robust feature extraction capabilities on graph structures and are extensively applied in HSI classification. However, one significant challenge in using GNNs is the selection of appropriate neighboring nodes for information aggregation. To address the existing challenges of GCN and CNN and leverage their respective advantages, this paper introduces a novel patch-based CNN-GCN fusion classification network, named multi-scale random-shape convolution and adaptive graph convolution fusion network (MRCAGCFN). It consists of a spectral transformation module and three main modules we proposed: a multi-scale random-shape convolution module for extracting convolution features, where the shape of the convolution kernel is randomized and a multi-scale approach is applied to enhance adaptability to data with diverse shapes; an adaptive feature-fusion graph convolution module for extracting graph convolution features, where the weights for neighborhood aggregation are learned adaptively to reduce feature fusion from dissimilar nodes and strengthen feature fusion from similar nodes; and an adaptive local feature processing module for processing features, where two different methods are employed to convert patch-level features to pixel-level features, thereby improving feature representation. MRCAGCFN combines the strengths of CNN and GCN while introducing enhancements to better accommodate diverse feature shapes. Experimental results on three HSI classification datasets demonstrate that our proposed MRCAGCFN outperforms some existing methods. The codes of our MRCAGCFN will be available at https://github.com/shengrunhua/MRCAGCFN.

Accurate segmentation of retinal images can assist ophthalmologists to determine the degree of retinopathy and diagnose other systemic diseases. However, the structure of the retina is complex, and different anatomical structures often affect the segmentation of fundus lesions. In this paper, a new segmentation strategy called a dual stream segmentation network embedded into a conditional generative adversarial network is proposed to improve the accuracy of retinal lesion segmentation. First, a dual stream encoder is proposed to utilize the capabilities of two different networks and extract more feature information. Second, a multiple level fuse block is proposed to decode the richer and more effective features from the two different parallel encoders. Third, the proposed network is further trained in a semi-supervised adversarial manner to leverage from labeled images and unlabeled images with high confident pseudo labels, which are selected by the dual stream Bayesian segmentation network. An annotation discriminator is further proposed to reduce the negativity that prediction tends to become increasingly similar to the inaccurate predictions of unlabeled images. The proposed method is cross-validated in 384 clinical fundus fluorescein angiography images and 1040 optical coherence tomography images. Compared to state-of-the-art methods, the proposed method can achieve better segmentation of retinal capillary non-perfusion region and choroidal neovascularization.