Universal Batch Steganalysis

Abstract

Steganography is the hiding of information within an innocent `cover': such a technology is becoming increasingly desirable for those who wish to evade network monitoring or to disguise their circle of correspondents. On the other hand, it presents a challenge for those with a legitimate need to monitor networks. The aim of steganalysis is to decide whether internet traffic contains hidden data, and the literature has developed rapidly in the last ten years. (For full literature surveys, see the papers [1-8].) However, it has developed in a particularly specialised direction, to binary classification (innocent cover vs. guilty stego) of single objects using machine learning methods, applied almost exclusively to grayscale images. The practical application of such a detector is uncertain, as it requires potentially unrealistic knowledge by the detector: the exact embedding method used, (usually) the size of the embedded payload, and training data from the same source as that being examined. Furthermore, it does not generalise well to detection of multiple objects.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2014
Accession Number
ADA616034

Entities

People

  • Andrew D. Ker
  • Tomas Pevny

Organizations

  • Czech Technical University in Prague

Tags

Communities of Interest

  • Autonomy
  • Cyber
  • Space

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Data Sets
  • Decoding
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Feature Extraction
  • Image Processing
  • Information Science
  • Kernel Functions
  • Machine Learning
  • Social Networking Services
  • Social Networks
  • Supervised Machine Learning
  • Training
  • Unsupervised Machine Learning

Readers

  • Cybersecurity.
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks