Summary of Pseudo-label Based Domain Adaptation For Zero-shot Text Steganalysis, by Yufei Luo et al.
Pseudo-label Based Domain Adaptation for Zero-Shot Text Steganalysis
by Yufei Luo, Zhen Yang, Ru Zhang, Jianyi Liu
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses challenges in text steganalysis by proposing a cross-domain analysis method based on pseudo-labeling and domain adaptation. The proposed PDTS model combines pre-trained BERT with a Bi-LSTM to learn generic features across tasks and generate task-specific representations. A feature filtering mechanism is designed to selectively propagate features, enhancing classification performance. The model is trained using labeled source data and adapted to target domain data distribution using pseudo-labels through self-training. Experimental results demonstrate the effectiveness of PDTS in zero-shot text steganalysis tasks, achieving high detection accuracy even without labeled data in the target domain, outperforming current methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem in analyzing hidden messages in texts. Right now, most ways to do this rely on big neural networks that need lots of labeled text examples to work well. But getting those labeled examples is often hard and expensive. Plus, even if we have enough data, models can still struggle to recognize new types of hidden messages. To fix these issues, the paper proposes a new way to analyze texts using a combination of two techniques: pseudo-labeling and domain adaptation. This approach allows our model to learn from labeled examples and adapt to new situations without needing more training data. The results show that this method is very effective at detecting hidden messages, even when we don’t have any labeled examples in the new situation. |
Keywords
» Artificial intelligence » Bert » Classification » Domain adaptation » Lstm » Self training » Zero shot
