An Intriguing Struggle of CNNs in JPEG Steganalysis and the OneHot Solution
Abstract
Deep convolutional neural networks (CNNs) have become the tool of choice for steganalysis because they outperform older feature-based detectors by a large margin. However, recent work points at cases where feature-based detectors perform better than CNNs due to their failure to compute simple statistics of DCT coefficients. We introduce a shallow OneHot CNN, which encodes DCT coefficients using clipped one-hot encoding into a binary volumetric representation of the DCT plane fed to a convolutional block designed to learn relevant intra-block and inter-block relationships using vanilla and dilated convolutions. Methodology for plugging the OneHot network into conventional steganalysis CNNs is also introduced for an end-to-end learnable detector with improved performance.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 2020
- Accession Number
- AD1124244
Entities
People
- Jessica Fridrich
- Yassine Yousfi