Abstract
The first joint fingerprinting and decryption (JFD) for vector quantization (VQ) images addressed the problem that the decrypted multimedia data may be redistributed from authorized customers to unauthorized customers. The scheme also caused conventional JFD methods to be equipped with a special ability to resist noise interference. Till now, some existing schemes related have been proposed to protect the multimedia content and distribution, but these schemes failed to tackle several problems existing in the original JFD scheme based on VQ image, including high transmission cost and severe fingerprinted image distortion. In this paper, we propose a novel JFD method by combining a weight-sum function with fingerprinting embedding and extraction for VQ images. Under the combination, the visual quality of the fingerprinted image is further improved; also the fingerprint extraction implements a blind extraction process. Experiments and analyses demonstrate the feasibility of the proposed method.
Highlights
The digitized form of numerous multimedia contents facilities various operations and allows fast distribution from one physical location to another via the Internet
Security and Communication Networks these schemes effectively resolve the problem of illegal distribution at the receiver end, they cannot be applied to the vector quantization (VQ) domain and the encrypted image cannot effectively resist noise interference
A new joint fingerprinting and decryption (JFD) scheme for VQ images was proposed by Lin et al in 2012 [14]
Summary
Security and Communication Networks these schemes effectively resolve the problem of illegal distribution at the receiver end, they cannot be applied to the VQ (vector quantization) domain and the encrypted image cannot effectively resist noise interference. To solve this problem, a new JFD scheme for VQ images was proposed by Lin et al in 2012 [14]. The major difference between the proposed method and scheme [14] is the way of how to efficiently embed fingerprint into codeword index and ensure the changeable maximum fingerprint capacity. Given the same embedding rate, the PSNR of our method is higher than that in the original scheme
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