nils-herrmann/clean_scirepeval huggingface dataset:
Video alignment for phylogenetic analysis

The possibility of studying multiple objects at once for forensic analysis has paved the way to the development of multimedia phylogeny algorithms. Concerning video phylogeny, a fundamental step at the base of many applications is multiple video alignment. This is, given a pool of near-duplicate video sequences partially overlapping in the temporal domain, find the relative time delay between all of them. As phylogeny methods typically takes into account huge quantities of data, the used alignment algorithms must be computationally efficient. In this paper, we propose a solution for multiple video alignment based on the minimisation of a least-square cost function. The proposed solution can be computed in closed form with reduced computational complexity. Moreover, we propose two possible solutions for refining the estimated alignment based on the removal of outlier measurements.
A blind and readable watermarking technique for color images

The paper presents a wavelet domain watermarking technique for color images. The characteristics of this transform domain are well suited for masking consideration, due to its good localization in both space and frequency. The different color sensitivity can be exploited in order to increase the watermarking power, and therefore its reliability. Thanks to the multi-resolution nature of the discrete wavelet transform (DWT) the watermark detection can be obtained iteratively. The technique has been successfully evaluated against attacks such as JPEG and JPEG-2000 compression, filtering, cropping, dithering and digital to analog and analog to digital conversions.
Codec and GOP Identification in Double Compressed Videos

Video content is routinely acquired and distributed in a digital compressed format. In many cases, the same video content is encoded multiple times. This is the typical scenario that arises when a video, originally encoded directly by the acquisition device, is then re-encoded, either after an editing operation, or when uploaded to a sharing website. The analysis of the bitstream reveals details of the last compression step (i.e., the codec adopted and the corresponding encoding parameters), while masking the previous compression history. Therefore, in this paper, we consider a processing chain of two coding steps, and we propose a method that exploits coding-based footprints to identify both the codec and the size of the group of pictures (GOPs) used in the first coding step. This sort of analysis is useful in video forensics, when the analyst is interested in determining the characteristics of the originating source device, and in video quality assessment, since quality is determined by the whole compression history. The proposed method relies on the fact that lossy coding is an (almost) idempotent operation. That is, re-encoding a video sequence with the same codec and coding parameters produces a sequence that is similar to the former. As a consequence, if the second codec in the chain does not significantly alter the sequence, it is possible to analyze this sort of similarity to identify the first codec and the adopted GOP size. The method was extensively validated on a very large data set of video sequences generated by encoding content with a diversity of codecs (MPEG-2, MPEG-4, H.264/AVC, and DIRAC) and different encoding parameters. In addition, a proof of concept showing that the proposed method can also be used on videos downloaded from YouTube is reported.
Antiforensics attacks to Benford's law for the detection of double compressed images

Researchers have been recently challenging the robustness of forensic algorithms by designing antiforensic strategies that try to fool them. In this paper, we propose an antiforensic strategy that targets double image compression detectors based on Benford's law (or first digit law). The proposed approach is able to modify the first digit statistics of the considered data (a double compressed image) to fool single/double compression detectors based on Benford's law. In this way, the proposed strategy tries to mimick the effects of a single compression with limited additional distortion. The presented algorithm performs better than previous state-of-the-art antiforensic strategies and can be easily extended to other fraud detection methods.
Exact localization of acoustic reflectors from quadratic constraints

In this paper we discuss a method for localizing acoustic reflectors in space based on acoustic measurements on source-to-microphone reflective paths. The method converts Time of Arrival (TOA) and Time Difference of Arrival (TDOA) into quadratic constraints on the line corresponding to the reflector. In order to be robust against measurement errors we derive an exact solution for the minimization of a cost function that combines an arbitrary number of quadratic constraints. Moreover we propose a new method for the analytic prediction of reflector localization accuracy. This method is sufficiently general to be applicable to a wide range of estimation problems.
Coding Local and Global Binary Visual Features Extracted From Video Sequences

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the bag-of-visual word model. Several applications, including, for example, visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget while attaining a target level of efficiency. In this paper, we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can conveniently be adopted to support the analyze-then-compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs the visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the compress-then-analyze (CTA) paradigm. In this paper, we experimentally compare the ATC and the CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: 1) homography estimation and 2) content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with the CTA, especially in bandwidth limited scenarios.
Tampering Detection and Localization Through Clustering of Camera-Based CNN Features

Due to the rapid proliferation of image capturing devices and user-friendly editing software suites, image manipulation is at everyone's hand. For this reason, the forensic community has developed a series of techniques to determine image authenticity. In this paper, we propose an algorithm for image tampering detection and localization, leveraging characteristic footprints left on images by different camera models. The rationale behind our algorithm is that all pixels of pristine images should be detected as being shot with a single device. Conversely, if a picture is obtained through image composition, traces of multiple devices can be detected. The proposed algorithm exploits a convolutional neural network (CNN) to extract characteristic camera model features from image patches. These features are then analyzed by means of iterative clustering techniques in order to detect whether an image has been forged, and localize the alien region.
Design of projection matrices for PRNU compression

Photo Response Non-Uniformity (PRNU) is the defacto standard in image source identification, allowing scientists, researchers, forensics investigators and courts to bind a picture under investigation to the specific camera sensor that took the shot at first place. Caused by silicon sensor imperfections, PRNU is characterized as a Gaussian i.i.d weak multiplicative noise embedded into every digital photo at acquisition time. Despite PRNU nearly-flat spectral characteristics, it undergoes several interpolations steps while image is demosaicked and optionally JPEG compressed. In this paper we propose a novel approach to the design of projection matrices tailored to PRNU compression. Joint effect of interpolation and projection on cross-correlation test is first analyzed, in order to derive those conditions that maximize detection while reducing false-alarm probability. A design methodology to build effective projection matrices is then presented, taking into account computational complexity. Validation of the proposed approach is finally performed against state-of-the-art methods on a well known public image dataset.
Detection of temporal interpolation in video sequences

Nowadays, considering the availability of relatively cheap devices and powerful editing software, video tampering is a relatively easy task. Video sequences can be tampered with by performing, e.g., temporal splicing. However, if the sequences spliced together do not share the same frame rate, they have to be temporally interpolated beforehand. This operation is often made using motion compensated interpolators, which allow to minimize visual artifacts. In this paper we propose a detector of this kind of interpolation. Moreover, the detector is capable of identifying the interpolation factor used, allowing an analyst to uncover the original frame rate of a sequence. This method relies on the analysis of the correlation introduced by the filter adopted by the interpolator. Results show that detection is successful, provided that the number of observed interpolated frames is large enough. Moreover, tests on compressed sequences obtained from television broadcasts validate the method in a real world scenario.
First Steps Toward Camera Model Identification With Convolutional Neural Networks

Detecting the camera model used to shoot a picture enables to solve a wide series of forensic problems, from copyright infringement to ownership attribution. For this reason, the forensic community has developed a set of camera model identification algorithms that exploit characteristic traces left on acquired images by the processing pipelines specific of each camera model. In this letter, we investigate a novel approach to solve camera model identification problem. Specifically, we propose a data-driven algorithm based on convolutional neural networks, which learns features characterizing each camera model directly from the acquired pictures. Results on a well-known dataset of 18 camera models show that: 1) the proposed method outperforms up-to-date state-of-the-art algorithms on classification of 64 × 64 color image patches; 2) features learned by the proposed network generalize to camera models never used for training.
No-Reference Video Quality Monitoring for H.264/AVC Coded Video

When video is transmitted over a packet-switched network, the sequence reconstructed at the receiver side might suffer from impairments introduced by packet losses, which can only be partially healed by the action of error concealment techniques. In this context we propose NORM (NO-Reference video quality Monitoring), an algorithm to assess the quality degradation of H.264/AVC video affected by channel errors. NORM works at the receiver side where both the original and the uncorrupted video content is unavailable. We explicitly account for distortion introduced by spatial and temporal error concealment together with the effect of temporal motion-compensation. NORM provides an estimate of the mean square error distortion at the macroblock level, showing good linear correlation (correlation coefficient greater than 0.80) with the distortion computed in full-reference mode. In addition, the estimate at the macroblock level can be successfully exploited by forward quality monitoring systems that compute quality objective metrics to predict mean opinion score (MOS) values. As a proof of concept, we feed the output of NORM to a reduced-reference quality monitoring system that computes an estimate of the structural similarity metric (SSIM) score, which is known to be well correlated with perceptual quality.
A robust video watermarking technique in the spatial domain

We present a scheme for robust watermark insertion into video sequences. The proposed approach works in the uncompressed image domain, and it is robust against a large set of possible attacks. It has been developed for two principle applications: copyright protection and invisible embedding of indexing information. The proposed scheme has been tested and the results show that a robust watermark can be embedded in video sequences, and extracted after different attacks, in particular MPEG compression and geometrical attacks like cropping and resizing. We also compared two different error correcting codes used to improve robustness: convolutional and turbo codes.
Semantic segmentation applied to image interpolation in the case of camera panning and zooming

An algorithm for image interpolation that takes into account both the motion of the camera and the motion of the imaged objects is described. First the estimated global motion parameters are used to compensate for the camera motion. The missing images are then interpolated by using the local motion field. Finally, these images are reconstructed in their correct dimension, considering the global motion information. Experiments have been carried out to extend the concept of object-background segmentation, developed for fixed camera sequences, to the case of camera zoom or pan. Simulations show that in order to obtain an effective semantic segmentation it is necessary to use a very accurate model of the camera lens system. The estimation of the global motion parameters is shown to be very accurate. For the interpolated images, an average gain of 4-5 dB with respect to the case of standard motion-compensated interpolation has been obtained.<<ETX>>
Landmine detection from GPR data using convolutional neural networks

The presence of buried landmines is a serious threat in many areas around the World. Despite various techniques have been proposed in the literature to detect and recognize buried objects, automatic and easy to use systems providing accurate performance are still under research. Given the incredible results achieved by deep learning in many detection tasks, in this paper we propose a pipeline for buried landmine detection based on convolutional neural networks (CNNs) applied to ground-penetrating radar (GPR) images. The proposed algorithm is capable of recognizing whether a B-scan profile obtained from GPR acquisitions contains traces of buried mines. Validation of the presented system is carried out on real GPR acquisitions, albeit system training can be performed simply relying on synthetically generated data. Results show that it is possible to reach 95% of detection accuracy without training in real acquisition of landmine profiles.
A H.264/AVC video database for the evaluation of quality metrics

This paper describes a publicly available database of subjective scores, relative to quality assessment of 156 video streams encoded with H.264/AVC and corrupted by simulating packet losses over an error-prone network. The data has been collected in controlled test environments at the premises of two academic institutions. A detailed statistical analysis of subjective results has been performed, showing high consistency of the collected scores. In addition to subjective scores, we have made available to the research community both the uncompressed files and the H.264/AVC bitstreams of each video sequence, in order to provide a common database of benchmark data to test and compare the performance of full-reference, reduced-reference and no-reference video quality assessment algorithms.
3D Beam Tracing Based on Visibility Lookup for Interactive Acoustic Modeling

We present a method for accelerating the computation of specular reflections in complex 3D enclosures, based on acoustic beam tracing. Our method constructs the beam tree on the fly through an iterative lookup process of a precomputed data structure that collects the information on the exact mutual visibility among all reflectors in the environment (region-to-region visibility). This information is encoded in the form of visibility regions that are conveniently represented in the space of acoustic rays using the Plücker coordinates. During the beam tracing phase, the visibility of the environment from the source position (the beam tree) is evaluated by traversing the precomputed visibility data structure and testing the presence of beams inside the visibility regions. The Plücker parameterization simplifies this procedure and reduces its computational burden, as it turns out to be an iterative intersection of linear subspaces. Similarly, during the path determination phase, acoustic paths are found by testing their presence within the nodes of the beam tree data structure. The simulations show that, with an average computation time per beam in the order of a dozen of microseconds, the proposed method can compute a large number of beams at rates suitable for interactive applications with moving sources and receivers.
A multiresolution level-set approach to surface fusion

We propose a novel solution to the problem of combining a number of partial reconstructions through a process of 3D "patchworking". Our method is able to seamlessly "sew" the surface overlaps together, and to reasonably "mend" all the holes that remain after surface assembly, which usually correspond to the non-visible portions of the object surface. The approach is based on the temporal evolution of the zero level set volumetric function, driven by surface curvature and distance from data and works entirely in a multi-resolution fashion.
Hash-based frame selection for video phylogeny

Multimedia phylogeny is a research field that aims at tracing back past history of multimedia documents to discover their ancestral relationships. As an example, it might leverage, with the aid of other side information, forensic analysts to detect who was the first user that published online an illegal content (e.g., child pornography). Although relatively well developed for images, this field is still not fully fledged when it comes to analyzing ancestral and evolutionary relationships among digital videos. Dealing with videos is much more challenging, especially as temporal dimension comes into play. In this vein, one of the pivotal tasks for video phylogeny reconstruction is video synchronization in order to compare temporally coherent near-duplicate frames among pairs of sequences. In this work, we propose an algorithm to efficiently select synchronized frame pairs among videos before calculating their phylogenetic relationships. This approach underpins the video phylogeny reconstruction and leverages the analysis on a reduced subset of frames rather than on the full set, thus decreasing the overall computational time. Experimental results show the effectiveness of the proposed method when temporal transformations are considered (i.e., change of frame rate and temporal clipping at any point in the stream).
Discriminating multiple JPEG compression using first digit features

The analysis of double-compressed images is a problem largely studied by the multimedia forensics community, as it might be exploited, e.g., for tampering localization or source device identification. In many practical scenarios, e.g. photos uploaded on blogs, on-line albums, and photo sharing Web sites, images might be compressed several times. However, the identification of the number of compression stages applied to an image remains an open issue. This paper proposes a forensic method based on the analysis of the distribution of the first significant digits of DCT coefficients, which is modeled according to Benford's law. The method relies on a set of Support Vector Machine (SVM) classifiers and allows us to accurately identify the number of compression stages applied to an image. Up to four consecutive compression stages were considered in the experimental validation. The proposed approach extends and outperforms the previously published methods aimed at detecting double JPEG compression.
Data-Driven Feature Characterization Techniques for Laser Printer Attribution

Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, as commonly proposed methods for this task are based on custom-tailored features, they are limited by modeling assumptions about printing artifacts. In this paper, we explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. This allows us to avoid any prior assumption about printing artifacts that characterize each printer, thus highlighting almost invisible and difficult printer footprints generated during the printing process. The proposed approach merges, in a synergistic fashion, convolutional neural networks (CNNs) applied on multiple representations of multiple data. Multiple representations, generated through different pre-processing operations, enable the use of the small and lightweight CNNs whilst the use of multiple data enable the use of aggregation procedures to better determine the provenance of a document. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem.
Piecewise distortion correction for fisheye lenses

Lens distortion is a well-known problem for camera calibration. In particular in applications where a large amount of low-quality acquisition devices is adopted, like, e.g. embedded systems or Cyber-Physical Systems (CPS), a complete re-sectioning and undistortion in different operating conditions (e.g. autofocus, zooming) could not be feasible and accurate. Usually undistortion is obtained building a proper invertible geometrical distortion model with a specific number of parameters, but, unfortunately with the actually available low cost wide-angle and ultra wide-angle lenses a simple mathematical model for a global closed form solution can be ineffective in practical cases in particular in the peripheral image regions. In order to account for these problems we present a novel local correction approach based on the planarity and orthogonality constrains for a planar target (a checkerboard) where a Look-Up Table (LUT) is built to provide the minimum displacement for every pixel in the target region minimizing interpolation and residual distortion. The proposed method can also be considered a preliminary step in order to recognize image elements before global undistortion e.g. for features matching in images stitching.
Extraction of acoustic sources for multiple arrays based on the ray space transform

In this paper we present a source extraction technique for multiple uniform linear arrays distributed in space. The technique adopts the Ray Space Transform representation of the sound field, which is inherently based on the Plane Wave Decomposition. The Ray Space Transform gives us an intuitive representation of the acoustic field, thus enabling the adoption of geometrically-motivated constraints in the spatial filter design. The proposed approach is semi-blind since it needs as input an estimate of the source positions. We prove the effectiveness of the proposed solution through simulations using both white noise and speech signals.