Quantization-Based Data Hiding Robust to Linear-Time-Invariant Filtering

Abstract

Quantization-based methods, such as dither modulation (DM), have gained wide acceptance due to their host rejection capabilities which afford significant performance gains over spread-spectrum-based methods in additive white Gaussian channels. Unfortunately, existing quantization-based schemes are not robust against simple linear-time-invariant (LTI) filtering, which is a common operation with multimedia signals. We propose a new algorithm, named discrete Fourier transform-rational dither modulation (DFT-RDM) which is robust against LTI filtering and yet does not assume any prior knowledge of the filter at either the embedder or the detector. DFT-RDM basically combines a DFT operation with a quantization-based scheme robust to amplitude scaling. Two easily implementable improvements over the basic DFT-RDM are proposed: windowing and spreading. In particular, the latter leads to performance gains that are much larger than those achieved with spreading in regular DM. We also provide a thorough analysis of our scheme which leads to both accurate predictions and bounds on the per-DFT-channel bit-error rate, for the basic DFT-RDM and its combination with spreading and windowing. These tools let the designer choose the main embedding parameters without actually requiring any simulation. The results of several simulations for practical filters validating our analysis are presented as well. The benefits of combining DFT-RDM with windowing, spreading, and Reed-Solomon channel coding are illustrated with an example.