Robust image alignment for tampering detection

The widespread use of classic and newest technologies available on Internet (e.g., emails, social networks, digital repositories) has induced a growing interest on systems able to protect the visual content against malicious manipulations that could be performed during their transmission. One of the main problems addressed in this context is the authentication of the image received in a communication. This task is usually performed by localizing the regions of the image which have been tampered. To this aim the aligned image should be first registered with the one at the sender by exploiting the information provided by a specific component of the forensic hash associated to the image. In this paper we propose a robust alignment method which makes use of an image hash component based on the Bag of Features paradigm. The proposed signature is attached to the image before transmission and then analyzed at destination to recover the geometric transformations which have been applied to the received image. The estimator is based on a voting procedure in the parameter space of the model used to recover the geometric transformation occurred into the manipulated image. The proposed image hash encodes the spatial distribution of the image features to deal with highly textured and contrasted tampering patterns. A block-wise tampering detection which exploits an histograms of oriented gradients representation is also proposed. A non-uniform quantization of the histogram of oriented gradient space is used to build the signature of each image block for tampering purposes. Experiments show that the proposed approach obtains good margin of performances with respect to state-of-the art methods.