Localization of sparse image tampering via random projections
Hashes can be used to provide authentication of multimedia contents. In the case of images, a hash can be used to detect whether the data has been modified in an illegitimate way. When the authentication check fails, it might be useful to localize the tampering in the spatial domain. This paper proposes an algorithm based on compressive sensing principles, which solves both the authentication and the localization problems. The encoder produces a hash using a small bit budget by quantizing a limited number of random projections of the authentic image. The decoder uses the hash to estimate the distortion between the original and the received image. In addition, if the attack is sparse, it can be also localized. In order to keep the size of the hash small, encoding/decoding takes advantage of distributed source codes. This paper also investigates experimentally the tradeoff between the rate allocated to the hash and the performance achieved in terms of tampering localization.