Convolutional neural networks (CNN) 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, we 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. We 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.

中文翻译：

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BitMix：图像隐写分析的数据增强

用于图像隐写分析的卷积神经网络 (CNN) 通过采用来自高级视觉任务的概念展示了更好的性能。主要采用的概念是使用数据增强来避免由于数据有限而导致的过度拟合。为了在不破坏消息嵌入的情况下增加数据，在隐写分析中仅使用旋转 90 度的倍数或水平翻转，从一个样本生成八个固定结果。为了克服这个限制，我们提出了 BitMix，一种用于空间图像隐写分析的数据增强方法。BitMix 通过交换随机补丁来混合覆盖和隐写图像对，并生成嵌入自适应标签，其中交换补丁中修改的像素数与覆盖-隐写对中的像素数之比。我们探索最佳超参数，在小批量中应用 BitMix 的比率，以及用于交换补丁的边界框的大小。结果表明，使用 BitMix 提高了空间图像隐写分析的性能，并且优于其他数据增强方法。