SINR, bit error rate, and shannon capacity optimized spread-spectrum steganography

Gkizeli, M.; Pados, Dimitris A.; Medley, Michael J.

doi:10.1109/icip.2004.1421364

Cited by 14 publications

(13 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, N , can be steered away from the embedding carrier using an operator of the form (I L − css T ) with a parameter c ∈ R and the carrier s ∈ R L . In parallel to (2), the composite signal of additive SS embedding on linearly transformed host data is [11,12,22] …”

Section: Ss Embedding On Linearly Transformed Hostmentioning

confidence: 99%

“…Proposition 2 Consider additive SS embedding in linearly transformed host data by (11), and secret message is recovered by matched filter. For any hidden messageinduced distortion budget D and any carrier s, the optimal amplitude A and transformation parameter c to maximize the SINR of matched filter are…”

Section: Ss Embedding On Linearly Transformed Hostmentioning

confidence: 99%

“…But for the precancelation with regular modulation (23) or SS embedding on linearly transformed host (11), the error probability can be approximated as BER = Q √ D σ n , which is smaller by a factor of 1/2. The probability of error of SS embedding with precoding is larger because there is an additional possibility of confusion across replicas.…”

Section: M-pam Ss Embedding With Precodingmentioning

confidence: 99%

“…Spread-spectrum (SS) data embedding [11,12,22,34,36] is a branch of transform domain data embedding family tree and enjoys wide popularity in data embedding community, especially for watermarking-related applications. It is derived from SS digital communication systems [13] and utilizes a modulated carrier to deposit one information symbol across a group of host data coefficients or a linearly transformed version of them.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, an optimal carrier for binary symbol data embedding was presented in [11,12] by exploiting the second-order statistics (SOS) of the host data. Inspired by this result, we develop an eigen-based optimal carrier design for M-PAM SS embedding in linear-transform host media by considering the characteristic of SOS of the host data.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal M-PAM Spread-Spectrum Data Embedding with Precoding

Liu

Guo

et al. 2015

Circuits Syst Signal Process

View full text Add to dashboard Cite

We consider M-level pulse amplitude modulation (M-PAM) spreadspectrum (SS) data embedding in transform domain host data. The process of data embedding can be viewed as delivering information through the channel including additive interference from host that is known to the embedder. We first utilize the knowledge of second-order statistics of host to design optimal carrier that maximizes the signal-to-interference-plus-noise ratio at the decoder end. Then, inspired by Tomlinson-Harashima precoding used in communication systems, a symbol-bysymbol precoding scheme is developed for M-PAM SS embedding to alleviate the impact of the interference which is explicitly known to embedder. For any given embedding carrier and host data, we aim at designing precoding algorithm to minimize the receiver bit error rate (BER) with any given host distortion budget, and conversely minimize the distortion at any target BER. Experimental studies demonstrate that the proposed precoded SS embedding approach can significantly improve BER performance over conventional SS embedding schemes.

show abstract

Section: Ss Embedding On Linearly Transformed Hostmentioning

confidence: 99%

Section: Ss Embedding On Linearly Transformed Hostmentioning

confidence: 99%

Section: M-pam Ss Embedding With Precodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal M-PAM Spread-Spectrum Data Embedding with Precoding

Liu

Guo

et al. 2015

Circuits Syst Signal Process

View full text Add to dashboard Cite

show abstract

Optimal multiuser spread-spectrum data hiding in digital images

Wei

Pados

Batalama

et al. 2014

Security Comm. Networks

View full text Add to dashboard Cite

In this work, we intend to carry out optimized spread-spectrum concealment of multiuser data under a given digital image. First, the overall image is pre-processed into transform-domain small blocks from which host vectors are obtained via zig-zag scanning vectorization. Multiuser data hiding is performed in the generated host vectors. Under this data hiding system model, we give an orthogonal set of embedding spread-spectrum signatures that achieves maximum sum signalto-interference-plus-noise ratio at the output of the linear-filter receivers for any fixed embedding amplitude values. Then, for any given total embedding distortion constraint, we present the optimal multi-signature assignment and amplitude allocation that maximizes the sum capacity of the concealment procedure. The practical implication of the results is sum signal-to-interference-plus-noise ratio, sum-capacity optimal multiuser/multi-signature spread-spectrum data hiding in the digital image medium. Numerical results demonstrate the effectiveness of the proposed methods.

show abstract