BitMix: data augmentation for image steganalysis

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Convolutional neural networks for image steganalysis demonstrate better performances with employing concepts from high-level vision tasks. The major employed concept is to use data augmentation to avoid overfitting due to limited data. To augment data without damaging the message embedding, only rotating multiples of 90 degrees or horizontally flipping are used in steganalysis, which generates eight fixed results from one sample. To overcome this limitation, the authors propose BitMix, a data augmentation method for spatial image steganalysis. BitMix mixes a cover and stego image pair by swapping the random patch and generates an embedding adaptive label with the ratio of the number of pixels modified in the swapped patch to those in the cover-stego pair. The authors explore optimal hyperparameters, the ratio of applying BitMix in the mini-batch, and the size of the bounding box for swapping patch. The results reveal that using BitMix improves the performance of spatial image steganalysis and better than other data augmentation methods.
Publisher
INST ENGINEERING TECHNOLOGY-IET
Issue Date
2020-11
Language
English
Article Type
Article
Citation

ELECTRONICS LETTERS, v.56, no.24

ISSN
0013-5194
DOI
10.1049/el.2020.1951
URI
http://hdl.handle.net/10203/279326
Appears in Collection
CS-Journal Papers(저널논문)
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