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DiffBCE: Difference contrastive learning for binary code embeddings
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DOI：10.1016/j.infsof.2025.107822.png)
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Abstract
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Binary code embedding plays a crucial role in binary similarity detection and software security analysis. However, conventional methods often suffer from scalability issues and depend heavily on large amounts of labeled data, limiting their practical deployment in real-world scenarios.
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