论文标题
NBC-Softmax：DarkWeb作者指纹和迁移跟踪
NBC-Softmax : Darkweb Author fingerprinting and migration tracking
论文作者
论文摘要
公制学习旨在从数据中学习距离，从而增强了基于相似性算法的性能。作者样式检测任务是一个公制的学习问题，在该问题中，具有较小的课内变体和较大阶层差异的学习样式特征对于实现更好的性能至关重要。最近，基于SoftMax损失的公制学习已成功用于样式检测。尽管SoftMax损失可以产生可分离的表示，但其判别能力相对较差。在这项工作中，我们提出了NBC-SoftMax，这是一种基于对比损失的聚类技术，用于软磁损失，它更直观，能够实现卓越的性能。我们的技术符合大量样品的标准，从而实现了块对比度，这被证明超过了配对的损失。它有效地使用了微型批次采样，并且可扩展。在4个DarkWeb社交论坛上进行的实验，与NBCSauthor一起使用拟议的NBC-SoftMax进行作者和Sybil检测，这表明我们的负面对比方法不断地优于使用相同网络体系结构的最先进方法。 我们的代码可公开可用：https：//github.com/gayanku/nbc-softmax
Metric learning aims to learn distances from the data, which enhances the performance of similarity-based algorithms. An author style detection task is a metric learning problem, where learning style features with small intra-class variations and larger inter-class differences is of great importance to achieve better performance. Recently, metric learning based on softmax loss has been used successfully for style detection. While softmax loss can produce separable representations, its discriminative power is relatively poor. In this work, we propose NBC-Softmax, a contrastive loss based clustering technique for softmax loss, which is more intuitive and able to achieve superior performance. Our technique meets the criterion for larger number of samples, thus achieving block contrastiveness, which is proven to outperform pair-wise losses. It uses mini-batch sampling effectively and is scalable. Experiments on 4 darkweb social forums, with NBCSAuthor that uses the proposed NBC-Softmax for author and sybil detection, shows that our negative block contrastive approach constantly outperforms state-of-the-art methods using the same network architecture. Our code is publicly available at : https://github.com/gayanku/NBC-Softmax
