A new decoder for the optimum recovery of nonadditive watermarks

• Published in: IEEE transactions on image processing Vol. 10; no. 5; pp. 755 - 766
• Main Authors: Barni, M., Bartolini, F., De Rosa, A., Piva, A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Authentication; Copyright protection; Correlation; Decision theory; Decoders; Decoding; Discrete cosine transforms; Exact sciences and technology; Frequency domain analysis; Image processing; Information, signal and communications theory; Optimization; Pixel; Probability; Robustness; Signal processing; Studies; Telecommunications and information theory; Testing; Watermarking; Neyman Pearson detection; Image processing; Information protection; Digital signature; Statistical decision; Decoding; Optimal detection; Signal detection; Correlation method
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/83.918568

Watermark detection, i.e., the detection of an invisible signal hidden within an image for copyright protection or data authentication, has classically been tackled by means of correlation-based techniques. Nevertheless, when watermark embedding does not obey an additive rule, or when the features the watermark is superimposed on do not follow a Gaussian pdf, correlation-based decoding is not the optimum choice. A new decoding algorithm is presented here which is optimum for nonadditive watermarks embedded in the magnitude of a set of full-frame DFT coefficients of the host image. By relying on statistical decision theory, the structure of the optimum is derived according to the Neyman-Pearson criterion, thus permitting to minimize the missed detection probability subject to a given false detection rate. The validity of the optimum decoder has been tested thoroughly to assess the improvement it permits to achieve from a robustness perspective. The results we obtained confirm the superiority of the novel algorithm with respect to classical correlation-based decoding.