Deep Convolutional Neural Network to Detect J-UNIWARD
This work addresses steganalysis for security applications, but it is incremental as it builds on existing CNN approaches for JPEG steganography detection.
The paper tackled the problem of detecting J-UNIWARD, a secure JPEG steganographic method, using convolutional neural networks (CNNs), and found that a 20-layer CNN outperformed feature-based methods and reduced error by 35% compared to a recent CNN on large-scale datasets.
This paper presents an empirical study on applying convolutional neural networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG steganographic method. Experiments guiding the architectural design of the CNNs have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of size 512x512. Results have verified that both the pooling method and the depth of the CNNs are critical for performance. Results have also proved that a 20-layer CNN, in general, outperforms the most sophisticated feature-based methods, but its advantage gradually diminishes on hard-to-detect cases. To show that the performance generalizes to large-scale databases and to different cover sizes, one experiment has been conducted on the CLS-LOC dataset of ImageNet containing more than one million covers cropped to unified size of 256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently proposed for large-scale JPEG steganalysis by 35%. Source code is available via GitHub: https://github.com/GuanshuoXu/deep_cnn_jpeg_steganalysis