Abstract
An automatic video-object oriented steganographic system is proposed for biometrics authentication over error-prone networks. Initially, the host video object is automatically extracted through analysis of videoconference sequences. Next, the biometric pattern corresponding to the segmented video object is encrypted by a chaotic cipher module. Afterwards, the encrypted biometric signal is inserted to the most significant wavelet coefficients of the video object, using its qualified significant wavelet trees (QSWTs). QSWTs provide both invisibility and significant resistance against lossy transmission and compression, conditions that are typical in error prone networks. Finally, the inverse discrete wavelet transform (IDWT) is applied to provide the stego-object. Experimental results under various losses and JPEG compression ratios indicate the security, robustness, and efficiency of the proposed biometrics hiding system.
Highlights
Person authentication is one of the most important issues in contemporary societies
In order to confront the problem of user authentication, in this paper, we propose an efficient wavelet-based steganographic method for biometric signals hiding in video objects, which focuses on optimizing the authentication rate of hidden biometric data over error prone transmissions
The proposed video-objects oriented biometric signals hiding scheme is examined in terms of security and efficiency
Summary
Person authentication is one of the most important issues in contemporary societies. It ensures that a system’s resources are not obtained fraudulently by illegal users. Again, no encryption is incorporated, it is easy to extract the hidden fingerprints Another interesting, but not resistant to compression, method is proposed in [24], where a remote multimodal biometrics authentication framework that works on the basis of fragile watermarking is designed. In order to confront the problem of user authentication, in this paper, we propose an efficient wavelet-based steganographic method for biometric signals hiding in video objects, which focuses on optimizing the authentication rate of hidden biometric data over error prone transmissions. (d) Resistance of steganographic biometrics systems to signal distortions has not been sufficiently investigated in the literature, a topic that is extensively considered in this paper By this way, the proposed scheme contributes to illustrate the perspective of encrypted biometrics authentication systems over error prone networks. If a wavelet coefficient xn(i, j) ∈ D at the coarsest scale is a parent of xn−1(p, q), where D is a subband labeled HLn, LHn, HHn, satisfy |xn(i, j)| > T1, |xn−1(p, q)| > T2 for given thresholds T1 and T2, xn(i, j) and its children are called a QSWT
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