Davide Cozzolino

Efficient Dense-Field Copy–Move Forgery Detection

We propose a new algorithm for the accurate detection and localization of copy-move forgeries, based on rotation-invariant features computed densely on the image. Dense-field techniques proposed in the literature guarantee a superior performance with respect to their keypoint-based counterparts, at the price of a much higher processing time, mostly due to the feature matching phase. To overcome this limitation, we resort here to a fast approximate nearest-neighbor search algorithm, PatchMatch, especially suited for the computation of dense fields over images. We adapt the matching algorithm to deal efficiently with invariant features, so as to achieve higher robustness with respect to rotations and scale changes. Moreover, leveraging on the smoothness of the output field, we implement a simplified and reliable postprocessing procedure. The experimental analysis, conducted on databases available online, proves the proposed technique to be at least as accurate, generally more robust, and typically much faster than the state-of-the-art dense-field references.

Pansharpening by Convolutional Neural Networks

A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices typical of remote sensing. Experiments on three representative datasets show the proposed method to provide very promising results, largely competitive with the current state of the art in terms of both full-reference and no-reference metrics, and also at a visual inspection.

Fast Adaptive Nonlocal SAR Despeckling

Despeckling techniques based on the nonlocal approach provide an excellent performance, but exhibit also a remarkable complexity, unsuited to time-critical applications. In this letter, we propose a fast nonlocal despeckling filter. Starting from the recent SAR-BM3D algorithm, we propose to use a variable-size search area driven by the activity level of each patch, and a probabilistic early termination approach that exploits speckle statistics in order to speed up block matching. Finally, the use of look-up tables helps in further reducing the processing costs. The technique proposed conjugates excellent performance and low complexity, as demonstrated on both simulated and real-world SAR images and on a dedicated SAR despeckling benchmark.

Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques

We propose an image forgery localization technique which fuses the outputs of three complementary tools, based on sensor noise, machine-learning and block-matching, respectively. To apply the sensor noise tool, a preliminary camera identification phase was required, followed by estimation of the camera fingerprint, and then forgery detection and localization. The machine-learning is based on a suitable local descriptor, while block-matching relies on the PatchMatch algorithm. A decision fusion strategy is then implemented, based on suitable reliability indexes associated with the binary masks. The proposed technique ranked first in phase 2 of the first Image Forensics Challenge organized in 2013 by the IEEE Information Forensics and Security Technical Committee (IFS-TC).

Image forgery detection through residual-based local descriptors and block-matching

We propose a new image forgery detection technique which fuses the outputs of two very diverse tools, based on machine learning and block-matching, respectively. The machine-learning tool builds upon some local descriptors recently proposed in the steganalysis field, which are selected and merged based on an ad hoc measure of reliability. The block-matching tool leverages on the patchmatch algorithm for fast search of candidate matchings. Both tools are fine-tuned so as to optimize their fusion which, in turn, exploits the respective strengths and weaknesses of each tool. The proposed technique ranked first in phase 1 of the first Image Forensics Challenge organized in 2013 by the IEEE Signal Processing Society.

Copy-move forgery detection based on PatchMatch

In this work we propose a new algorithm for copy-move forgery detection and localization, based on the fast computation of a dense nearest-neighbor field. To this end, we use PatchMatch, an iterative randomized algorithm for nearest-neighbor search, which exploits the regularity of natural images to converge very rapidly to a near-optimal and smooth field. We modify the basic algorithm to gain robustness against rotations, while keeping the original computational efficiency. Experimental results show the proposed technique to outperform almost uniformly all tested reference techniques in terms of both accuracy and speed.

A feature-based approach for image tampering detection and localization

We propose a new camera-based technique for tampering localization. A large number of blocks are extracted off-line from training images and characterized through features based on a dense local descriptor. A multidimensional Gaussian model is then fit to the training features. In the testing phase, the image is analyzed in sliding-window modality: for each block, the log-likelihood of the associated feature is computed, reprojected in the image domain, and aggregated, so as to form a smooth decision map. Eventually, the tampering is localized by simple thresholding. Experiments carried out in a number of situation of interest show promising results.

Guided filtering for PRNU-based localization of small-size image forgeries

PRNU-based techniques guarantee a good forgery detection performance irrespective of the specific type of forgery. The presence or absence of the camera PRNU pattern is detected by a correlation test. Given the very low power of the PRNU signal, however, the correlation must be averaged over a pretty large window, reducing the algorithms ability to reveal small forgeries. To improve resolution, we estimate correlation with a spatially adaptive filtering technique, with weights computed over a suitable pilot image. Implementation efficiency is achieved by resorting to the recently proposed guided filters. Experiments prove that the proposed filtering strategy allows for a much better detection performance in the case of small forgeries.

Splicebuster: A new blind image splicing detector

We propose a new feature-based algorithm to detect image splicings without any prior information. Local features are computed from the co-occurrence of image residuals and used to extract synthetic feature parameters. Splicing and host images are assumed to be characterized by different parameters. These are learned by the image itself through the expectation-maximization algorithm together with the segmentation in genuine and spliced parts. A supervised version of the algorithm is also proposed. Preliminary results on a wide range of test images are very encouraging, showing that a limited-size, but meaningful, learning set may be sufficient for reliable splicing localization.

Multiple Classifier Systems for Image Forgery Detection

A large number of techniques have been proposed recently for forgery detection, based on widely different principles and processing tools. As a result, each technique performs well with some types of forgery, and under given hypotheses, and much worse in other situations. To improve robustness, one can merge the output of different techniques but it is not obvious how to balance the different sources of information. In this paper we consider and test several combining rules, working both at the abstract level and at measurement level, and providing information on both presence and location of suspect tampered regions. Experimental results on a suitable dataset of forged images show that a careful fusion of detector’s output largely outperforms individual detectors, and that measurement-level fusion methods are more effective than abstract-level ones.