Video Authentication Using Relative Correlation Information and SVM

Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

The Nature of Statistical Learning Theory

Two kinds of contemporary developments in cryptography are examined. Widening applications of teleprocessing have given rise to a need for new types of cryptographic systems, which minimize the need for secure key distribution channels and supply the equivalent of a written signature. This paper suggests ways to solve these currently open problems. It also discusses how the theories of communication and computation are beginning to provide the tools to solve cryptographic problems of long standing.

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New Directions in Cryptography

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Image Features From Phase Congruency

The existing digital data verification methods are able to detect regions that have been tampered with, but are too fragile to resist incidental manipulations. This paper proposes a new digital signature scheme which makes use of an image's contents (in the wavelet transform domain) to construct a structural digital signature (SDS) for image authentication. The characteristic of the SDS is that it can tolerate content-preserving modifications while detecting content-changing modifications. Many incidental manipulations, which were detected as malicious modifications in the previous digital signature verification or fragile watermarking schemes, can be bypassed in the proposed scheme. Performance analysis is conducted and experimental results show that the new scheme is indeed superb for image authentication.

/pdf/structural-digital-signature-for-image-authentication-an-1umfzobse9.pdf

Structural digital signature for image authentication: an incidental distortion resistant scheme

It is straightforward to apply general schemes for authenticating digital data to the problem of authenticating digital images. However, such a scheme would not authenticate images that have undergone lossy compression, even though they may not have been manipulated otherwise. We propose a scheme for authenticating the visual content of digital images. This scheme is robust to compression noise, but will detect deliberate manipulation of the image-data. The proposed scheme is based on the extraction of feature-points from the image. These feature-points are defined so as to be relatively unaffected by lossy compression. The set of feature-points from a given image is encrypted using public key encryption, to generate the digital signature of the image. Authenticity is verified by comparing the feature-points of the image in question, with those recovered from the previously computed digital signature.

Video Authentication Using Relative Correlation Information and SVM

References

The Nature of Statistical Learning Theory

New Directions in Cryptography

Image Features From Phase Congruency

Structural digital signature for image authentication: an incidental distortion resistant scheme

Compression tolerant image authentication

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The Nature of Statistical Learning Theory

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