Publications by authors named "Chunyang Qi"

Article Synopsis
  • The rise in the use of deep neural networks (DNNs) has increased the risk of privacy breaches, highlighting the need for better intellectual property protection.
  • Existing neural network watermarking techniques offer only passive protection, making proactive solutions essential to prevent unauthorized use.
  • The proposed SecureNet framework introduces a key-based access system for DNNs, allowing functionality only with the correct license key, while also providing various defense mechanisms and better performance than traditional encryption methods.
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Physical layer security (PLS) is considered one of the most promising solutions to solve the security problems of massive Internet of Things (IoTs) devices because of its lightweight and high efficiency. Significantly, the recent physical layer key generation (PLKG) scheme based on transmission delay proposed by Huang et al. (2021) does not have any restrictions on communication methods and can extend the traditional physical layer security based on wireless channels to the whole Internet scene.

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The family homology determination of malware has become a research hotspot as the number of malware variants are on the rise. However, existing studies on malware visualization only determines homology based on the global structure features of executable, which leads creators of some malware variants with the same structure intentionally set to misclassify them as the same family. We sought to develop a homology determination method using the fusion of global structure features and local fine-grained features based on malware visualization.

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Depth estimation of a single image presents a classic problem for computer vision, and is important for the 3D reconstruction of scenes, augmented reality, and object detection. At present, most researchers are beginning to focus on unsupervised monocular depth estimation. This paper proposes solutions to the current depth estimation problem.

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