One of the most common problems in the image forensics field is the reconstruction of the history of an image or a video. The data related to the characteristics of the camera that carried out the shooting, together with the reconstruction of the (possible) further processing, allow us to have some useful hints about the originality of the visual document under analysis. For example, if an image has been subjected to more than one JPEG compression, we can state that the considered image is not the exact bitstream generated by the camera at the time of shooting. It is then useful to estimate the quantization steps of the first compression, which, in case of JPEG images edited and then saved again in the same format, are no more available in the embedded metadata. In this paper, we present a novel algorithm to achieve this goal in case of double JPEG compressed images. The proposed approach copes with the case when the second quantization step is lower than the first one, exploiting the effects of successive quantizations followed by dequantizations. To improve the results of the estimation, a proper filtering strategy together with a function devoted to find the first quantization step, have been designed. Experimental results and comparisons with the state-of-the-art methods, confirm the effectiveness of the proposed approach.

Digital video stabilization enables to acquire video sequences without disturbing jerkiness by compensating unwanted camera movements. In this paper, we propose a novel fast image registration algorithm based on block matching. Unreliable motion vectors (i.e., not related with jitter movements) are properly filtered out by making use of ad-hoc rules taking into account local similarity, local “activity,” and matching effectiveness. Moreover, a temporal analysis of the relative error computed at each frame has been performed. Reliable information is then used to retrieve inter-frame transformation parameters. Experiments on real cases confirm the effectiveness of the proposed approach even in critical conditions.

This paper describes both hardware and software components to detect counterfeits of Euro banknotes. The proposed system is also able to recognize the banknote values. Differently than other state-of-the-art methods, the proposed approach makes use of banknote images acquired with a near infrared camera to perform recognition and authentication. This allows one to build a system that can effectively deal with real forgeries, which are usually not detectable with visible light. The hardware does not use any mechanical parts, so the overall system is low-cost. The proposed solution is reliable for ambient light and banknote positioning. Users should simply lean the banknote to be analyzed on a flat glass, and the system detects forgery, as well as recognizes the banknote value. The effectiveness of the proposed solution has been properly tested on a dataset composed by genuine and fake Euro banknotes provided by Italy's central bank.

The widespread diffusion of hand-held devices with video recording capabilities requires the adoption of reliable digital Stabilization methods to enjoy the acquired sequences without disturbing jerkiness. In order to effectively get rid of the unwanted camera movements, an estimate of the global motion between adjacent frames is necessary. This paper presents a novel approach for estimating the global motion between frames using a curve warping technique known as dynamic time warping. The proposed algorithm guarantees robustness also in presence of sharp illumination changes and moving objects.

The processing pipeline of a digital camera converts the RAW image acquired by the sensor to a representation of the original scene that should be as faithful as possible. There are mainly two modules responsible for the color-rendering accuracy of a digital camera: the former is the illuminant estimation and correction module, and the latter is the color matrix transformation aimed to adapt the color response of the sensor to a standard color space. These two modules together form what may be called the color correction pipeline. We design and test new color correction pipelines that exploit different illuminant estimation and correction algorithms that are tuned and automatically selected on the basis of the image content. Since the illuminant estimation is an ill-posed problem, illuminant correction is not error-free. An adaptive color matrix transformation module is optimized, taking into account the behavior of the first module in order to alleviate the amplification of color errors. The proposed pipelines are tested on a publicly available dataset of RAW images. Experimental results show that exploiting the cross-talks between the modules of the pipeline can lead to a higher color-rendition accuracy.

Digital video stabilization allows to acquire video sequences without disturbing jerkiness, removing unwanted camera movements. In this paper we propose a novel fast video stabilization algorithm based on block matching of local motion vectors. Some of these vectors are properly filtered out by making use of ad-hoc rules taking into account local similarity, local “activity” and matching effectiveness. Also a temporal analysis of the relative error computed at each frame has been achieved. Reliable information are then used to retrieve inter-frame transformation parameters. Experiments on real cases confirm the effectiveness of the proposed approach even in critical conditions.