PN-sequence masked spread-spectrum data embedding

Conventional additive spread-spectrum (SS) data embedding has a dangerous security flaw that unauthorized receivers can blindly extract hidden information without the knowledge of carrier(s). In this paper, pseudo-noise (PN) masking technique is adopted as an efficient security measure against illegitimate data extraction. The proposed PN-sequence masked SS embedding can offer efficient security against current SS embedding analysis without inducing any additional distortion to host nor notable recovery performance loss. To further improve recovery performance, optimal carrier design for PN-masked SS embedding is also developed. With any given host distortion budget, we aim at designing a carrier to maximize the output signal-to-interference-plus-noise ratio (SINR) of the corresponding maximum-SINR linear filter. Then, we present jointly optimal carrier and linear processor designs for PN-masked SS embedding in linearly modified transform domain host data. The extensive experimental studies confirm our analytical performance predictions and illustrate the benefits of the designed PN masked optimal SS embedding.