Mr. Haoxin Luo Profile

Haoxin Luo

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Proceedings Article | 21 December 2023 Paper

CFF: a cross-regional feature fusion network for face anti-spoofing

Haohai Wu, Qianyu Yu, Shaohao Xie, Haoxin Luo, Jubin Huang, Kaibin Xu

Proceedings Volume 12970, 129703T (2023) https://doi.org/10.1117/12.3012468

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With the rapid advancement of artificial intelligence, facial recognition technology has permeated various domains, revolutionizing our daily lives. However, this convenience comes hand in hand with security concerns. The emergence of facial spoofing attacks has raised serious issues concerning information security, financial integrity, and personal safety. To mitigate the risks associated with facial spoofing attacks, this research paper proposes innovative approaches and strategies to tackle this problem. To solve the problem of face spoofing attacks, the following work is proposed in this paper. Firstly, this work presents a novel framework named self-adaptive feature enhancement for FAS. This framework used RGB, depth, and reflection channels together by a feature extractor module. What’s more, this paper proposed a Cross-regional Feature Fusion (CFF) network, which added self-attention from vision transformer to improve the classification efficiency. Finally, the effectiveness of the proposed approaches and strategies is demonstrated through experimental results. The conducted experiments on several public datasets validate the success and performance of this research work.

Proceedings Article | 10 October 2023 Paper

A malicious programs detection method incorporating transformer and co-occurrence matrix

Changyi Zhong, Qianyu Yu, Haoxin Luo, Shaohao Xie

Proceedings Volume 12799, 127992Q (2023) https://doi.org/10.1117/12.3005851

KEYWORDS: Transformers, Cooccurrence matrices, Data modeling, Visual process modeling, Feature extraction

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To address the problem that traditional malicious program detection algorithms require a lot of specialized domain knowledge and convolutional neural networks for detecting malicious programs that are difficult to capture global features and long-range dependencies, we propose a malicious program detection method based on Transformer architecture and innovatively use the program assembly opcode frequency to construct a co-occurrence matrix, which in turn generates images as model inputs. Using the multi-headed attention mechanism of Transformer, we extract the malicious program opcode invocation pattern implied behind the co-occurrence matrix image and thus achieve the detection of malicious programs. The method can adapt to PE files of different sizes with fewer parameters and outperforms ViT-B/16 with an accuracy of 0.9887, precision of 0.9873, and F1 score of 0.9849.

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