Adaptive Steganography
Abstract
The research conducted under this grant has addressed the area of Adaptive Steganography, covering the domains of data hiding, data embedding, and forensics. Key accomplishments include the development of an entirely new and novel framework for data hiding using a set theoretic feasibility and optimization framework, forensic analyses encompassing both device/print forensics, extensions to media security and hardcopy data encoding. Of these accomplishments, the new framework for data hiding using a set theoretic feasibility framework represents a major advance in the field of data hiding that has led to the development of a new conceptual framework for data hiding based on signal processing considerations as opposed to the commonly accepted communications framework. The communications framework considers the data hiding problem purely from a communications perspective and uses ad hoc modifications to incorporate the perceptual signal processing constraints, which are not integral to the framework. The versatility of this framework has been demonstrated through its applications in data hiding, steganography, and fingerprinting. The framework encompasses both spread spectrum and Quantization Index Modulation (QIM) watermarking methods and also enables extensions to optimality where one criterion is optimized while maintaining other constraints. The overall work has resulted in ten journal and conference publications.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 28, 2011
- Accession Number
- ADA550303
Entities
People
- Gaurav Sharma
Organizations
- University of Rochester