Defect detection methods for smart contracts based on multimodality

Sidy Tambadou; Xueyuan Zhang; Guanghui Wang; Fang Zuo

doi:10.1117/12.3033729

13 June 2024 Defect detection methods for smart contracts based on multimodality

Sidy Tambadou, Xueyuan Zhang, Guanghui Wang, Fang Zuo

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131806Q (2024) https://doi.org/10.1117/12.3033729
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

With the popularization of blockchain technology, the application of smart contracts in various scenarios is becoming increasingly widespread. However, due to its inherent complexity and dynamism, the security issues of smart contracts are gradually becoming prominent. In order to effectively detect and repair defects in smart contracts, this paper proposes a multimodal smart contract defect detection method. This method integrates multimodal data information, including visual, semantic, and inheritance relationship structural information. It combines the Transform method to comprehensively analyze and identify potential defects in the operational status of smart contracts. Compared with other mainstream methods, it has been proven that our method can more comprehensively identify and locate defects and improve detection accuracy and coverage.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sidy Tambadou, Xueyuan Zhang, Guanghui Wang, and Fang Zuo "Defect detection methods for smart contracts based on multimodality", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131806Q (13 June 2024); https://doi.org/10.1117/12.3033729

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KEYWORDS

Feature extraction

Defect detection

Information visualization

Performance modeling

RGB color model

Data modeling

Visualization

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