Deep Convolutional Neural Network to Detect J-UNIWARD
Guanshuo Xu · 2017
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 512×512. 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 256×256. 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