Spatial models for localization of image tampering using distributed source codes

Media authentication is important in content delivery via untrusted intermediaries, such as peer-to-peer (P2P) file sharing. Many differently encoded versions of a media file might exist. Our previous work applied distributed source coding not only to distinguish the legitimate diversity of encoded images from tampering but also localize the tampered regions in an image already deemed to be inauthentic. An authentication decoder was supplied with a Slepian-Wolf encoded image projection as authentication data. A localization decoder required only incremental localization data beyond the authentication data since we use rate-adaptive distributed source codes. We extend the localization decoder with 1D and 2D spatial models to exploit the contiguity of the tampered regions. Our results show that the spatial decoders save 10% to 17% of authentication plus localization data size and offer greater confidence in tampering localization.

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