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AAS: Automatic Virtual Data Augmentation for Deep Image Steganalysis
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AI
DOI：10.1109/TDSC.2023.3333913.png)
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Abstract
En 中文
In recent years, steganalysis based on deep learning has evolved rapidly. However, training deep learning models is data-consuming. The models are prone to overfitting when data is limited. Data augmentation is an effective method to mitigate overfitting. Existing data augmentation methods in steganalysis can be categorized into cover enrichment and virtual augmentation. They are used in different stages. Cover enrichment refers to introducing additional cover-stego pairs in some ways, which is performed prior to training. In contrast, virtual augmentation augments data during training. Existing virtual augmentation methods are designed heuristically and rely on expert knowledge. In this paper, we propose the first automatic virtual data augmentation method for steganalysis. Specifically, we design an augmentation network that augments cover and stego images by intelligently adding noises. The augmentation network is trained adversarially with the steganalyzer to generate diverse data. Meanwhile, a class-invariant module prevents the augmentation network from changing the original data distribution too much. A stabilizer loss function is designed that keeps the adversarial training stable by constraining the number of noises. The experimental results show that the proposed method outperforms existing virtual augmentation methods. Moreover, combining the proposed method and cover enrichment can further boost performance.
Keywords:
Training
Data augmentation
Feature extraction
Deep learning
Task analysis
Data models
Transform coding
Virtual data augmentation
automatic
steganography
steganalysis
Journal
IF:
7.5
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2.4K
Citations:
9.6K