nils-herrmann/clean_scirepeval huggingface dataset:
Hash-Based Identification of Sparse Image Tampering

In the last decade, the increased possibility to produce, edit, and disseminate multimedia contents has not been adequately balanced by similar advances in protecting these contents from unauthorized diffusion of forged copies. When the goal is to detect whether or not a digital content has been tampered with in order to alter its semantics, the use of multimedia hashes turns out to be an effective solution to offer proof of legitimacy and to possibly identify the introduced tampering. We propose an image hashing algorithm based on compressive sensing principles, which solves both the authentication and the tampering identification problems. The original content producer generates a hash using a small bit budget by quantizing a limited number of random projections of the authentic image. The content user receives the (possibly altered) image and uses the hash to estimate the mean square error distortion between the original and the received image. In addition, if the introduced tampering is sparse in some orthonormal basis or redundant dictionary, an approximation is given in the pixel domain. We emphasize that the hash is universal, e.g., the same hash signature can be used to detect and identify different types of tampering. At the cost of additional complexity at the decoder, the proposed algorithm is robust to moderate content-preserving transformations including cropping, scaling, and rotation. In addition, in order to keep the size of the hash small, hash encoding/decoding takes advantage of distributed source codes.
Progressive image transmission based on image projections

We present a progressive image transmission coder, where geometrical vector quantization is applied to overlapped image projections. Progressive image transmission is widely used in many applications, because it offers successive improved reconstructions of an image. It is possible to have a rough version of information available in a very short time and, then, the viewer can decide whether further transmission is necessary or not. In the proposed coding scheme the still image is subdivided into circular partially overlapped regions and, then, some projections along predefined directions are determined and vector quantized by using geometrical VQ. The utilization of overlapped circular regions exploits the spatial redundancy present on the image, as the block subdivision does, and at the same time, it avoids the problem of blocking artifacts. The projection technique allows the detection of the most important information locally present on the image.<<ETX>>
Multi-Clue Image Tampering Localization

Image tampering is nowadays at everyone's reach. This has determined an urgent need of tools capable of revealing such alterations. Unfortunately, while forgeries can be operated in many different ways, forensic tools usually focus on one specific kind of forgeries. Therefore, an effective strategy for tampering detection and localization requires to merge the output of many different forensic tools. In this paper, we propose an algorithm for image tampering localization, based on the fusion of three separate detectors: i) one based on PRNU, working when we have at least a few of pictures shot with the same camera; ii) one based on PatchMatch; iii) one exploiting image phylogeny analysis, in case we have a set of near-duplicate images to analyze. The method is validated against the dataset released by the IEEE Information Forensics and Security Technical Committee for the First Image Forensics Challenge. Results show that the proposed algorithm can beat the challenge with the highest score achieved at paper submission time.
Some experiments on vector quantization using neural nets

Some applications of neural network algorithms for the design of vector quantizer codebooks are presented. Vector quantization has been applied to the problem of displaying natural images with a reduced set of colors (color palette) and to the interframe coding of image sequences. Self-organizing feature maps and competitive learning (CL) algorithms have been used for the codebook design and their results are compared with those obtained using a classical LBG (Linde, Buzo, Gray) algorithm. The best results are obtained using a CL algorithm with a new initialization strategy. With this technique the codebook design is very fast, and some experiments have been carried out in order to introduce adaptive vector quantization in the interframe coder. Frame by frame the codebooks used to quantize the most important information are updated and then the more active parts of the motion-compensated luminance differences are quantized with these new codebooks. A considerable rise of the coder performance was obtained.<<ETX>>
A low-cost solution to 3D pinna modeling for HRTF prediction

We propose an infrared (IR) stereo-vision system for estimating the 3D model of the pinna, based on low-cost devices. A commercial IR calibrated stereo camera is used in conjunction with a structured IR light projector, to acquire highly textured snapshots of the pinna. A point cloud is computed for each snapshot by triangulating the stereo correspondences detected in the acquired IR images. A complete 3D model is computed by aligning and merging the point clouds, and then creating a polygonal mesh surface. The nominal accuracy of the proposed system turns to be about 1 mm, which enables an accurate prediction of the Head Related Transfer Function (HRTF) through numerical acoustic simulation.
Geometric and radiometric modeling of 3D scenes

Modeling of 3D scenes is a hot topic in Computer Vision from more that thirty years, and probably its history is longer than a century considering also photogrammetry . In the recent years the rapid technological improvements that characterized the acquisition devices (photo-cameras, video-cameras, ..), illumination devices (lasers, structured light sources) and computational units allowed the application of 3D shape estimation methods, based on image analysis techniques, in a wide set of applications. Furthermore real-time 3D analysis is becoming a common tool in Virtual and Augmented Reality contexts. Aim of this presentation is a rapid description of recent major advances on geometric and radiometric modeling of 3D scenes based on image analysis.
Estimating channel-induced distortion in H.264/AVC video without bitstream information

No-reference video quality monitoring algorithms typically assume the availability of the encoded bitstream in order to assess the quality of the received signal at the decoder side. In some situations this is not possible, e.g. because the bitstream is encrypted or processed by third party decoders. Thus no-reference quality monitoring must be carried out in a blind way, i.e. using only pixel-domain data output by the decoder. In this paper we target this scenario for the specific case of distortion introduced by channel losses. We estimate the missing coding parameters, as well as the channel error pattern, and feed them into a no-reference quality monitoring system which produces accurate estimates of the MSE distortion. The results produced by the proposed method are well correlated (linear correlation coefficient larger than 0.8 over a wide range of packet loss rates) with the distortion computed in full-reference mode.
Local tampering detection in video sequences

Video sequences are often believed to provide stronger forensic evidence than still images, e.g., when used in lawsuits. However, a wide set of powerful and easy-to-use video authoring tools is today available to anyone. Therefore, it is possible for an attacker to maliciously forge a video sequence, e.g., by removing or inserting an object in a scene. These forms of manipulation can be performed with different techniques. For example, a portion of the original video may be replaced by either a still image repeated in time or, in more complex cases, by a video sequence. Moreover, the attacker might use as source data either a spatio-temporal region of the same video, or a region taken from an external sequence. In this paper we present the analysis of the footprints left when tampering with a video sequence, and propose a detection algorithm that allows a forensic analyst to reveal video forgeries and localize them in the spatio-temporal domain. With respect to the state-of-the-art, the proposed method is completely unsupervised and proves to be robust to compression. The algorithm is validated against a dataset of forged videos available online.
Estimation of room dimensions from a single impulse response

In this paper we propose a methodology for the estimation of the geometry of an environment based on a single Acoustic Impulse Response (AIR). The estimation algorithm makes use of tools for the modeling of propagation based on geometrical acoustics. A suitable cost function evaluates the distance between the simulated and measured AIRs. The room minimizing the cost function is chosen as the correct one. The cost function is strongly non linear. As a consequence, in order to reduce the complexity of the minimization problem, the algorithm needs a hypothesis about the class of geometry of the environment under analysis, such as rectangular or L-shaped rooms. We prove the effectiveness of the proposed algorithm with a number of simulations with increasing complexity.
Accurate and fast audio-realistic rendering of sounds in virtual environments

In this paper we propose a novel method for real-time auralization of sounds in complex environments using visibility diagrams. The method accounts for both specular and diffracted reflections with receivers and sources that are free to move. Our solution concentrates in a pre-processing phase all the operations that can be conducted without knowledge of either source or receiver locations. In fact we pre-compute a set of visibility diagrams and diffracted beam trees. Once the source location is specified, we can determine the reflective beam trees through a simple lookup process on the visibility diagrams. The additional knowledge of the receiver location allows us to immediately generate all reflective and diffractive paths that link source and receiver.
Synthesis of virtual views using non-Lambertian reflectivity models and stereo matching

A technique for the synthesis of virtual views of a 3D scene, starting from images taken by a calibrated multicamera system, is proposed and tested. Surface interpolation is performed over a set of 3D edges, computed with stereometric algorithms, and additional curvature-tuning points, scattered where the reflectivity model is sufficiently reliable. The 3D coordinates of the tuning points are computed by minimizing the MSE between the available real views and the corresponding synthetic views. Synthesis is finally carried out by reprojecting on the new image plane the estimated object surface over which texture-mapping of the reflection-corrected luminance function has been performed. Texture correction, which uses an estimate of a non-Lambertian reflectivity model, is done in such a way to simulate the migration of reflections due to the change of viewpoint. The technique has been tested on real images, producing realistic synthesized views.
No-Reference Pixel Video Quality Monitoring of Channel-Induced Distortion

Video transmitted over an error-prone network may be received at the decoder with degradations due to packet losses. No-reference quality monitoring algorithms are the most practical way to measure the quality of the received video, since they do not impose any change with respect to the network architecture. Conventionally, these methods assume the availability of the corrupted bitstream. In some situations this is not possible, e.g., because the bitstream is encrypted or processed by third-party decoders, and only the decoded pixel values can be used. The major issue in this scenario is the lack of knowledge about which regions of the video have been actually lost, which is a fundamental ingredient for estimating channel-induced distortion. In this paper, we propose a maximum a posteriori estimation of the pattern of lost macroblocks, which assumes the knowledge of the decoded pixels only. This information can be used as input to a no-reference quality monitoring system, which produces an accurate estimate of the mean-square-error (MSE) distortion introduced by channel errors. The results of the proposed method are well correlated with the MSE distortion computed in full-reference mode, with a linear correlation coefficient equal to 0.9 at frame level and 0.98 at sequence level.
Scalable coding of variable size blocks motion vectors

In this paper we discuss an algorithm that is able to provide a scalable (multiresolution) representation of the motion field information. It has been recently demonstrated that, for an open loop wavelet video coder, it is possible to use at the decoder side a scaled version of the original motion information and the residual coefficients computed with the full resolution/quality motion field without incurring into drift. We propose a fully scalable wavelet based video coder that performs motion estimation-compensation in the wavelet domain. In particular the coding scheme is specifically designed for variable size block matching algorithms. In this scenario motion vectors are distributed across an irregular lattice according to a quadtree structure. The developed system allows a scalable representation of the motion field and a flexible allocation of the bit budget between motion and residual data. The simulations that we have carried out show, at low bit-rates, a significant gain of the proposed approach with respect to the case in which the motion information is coded lossless.
Multi-camera systems

In this article, we present some simple and effective techniques for accurately calibrating a multi-camera acquisition system. The proposed methods were proven to be capable of accurate results even when using very simple calibration target sets and low-cost imaging devices, such as standard TV-resolution cameras connected to commercial frame-grabbers. In fact, the performance of our calibration approach yielded results that were about the same as that of other traditional calibration methods based on large 3D target sets. The proposed calibration strategy is based on a multi-view multi-camera approach. This was based on the analysis of a number of views of a simple calibration target-set placed in different (unknown) positions. Furthermore, the method is based on a self-calibration approach, which can refine the a priori knowledge of the world coordinates of the targets (even when such information is very poor) while estimating the parameters of the camera model. Finally we proposed a method, to make the calibration technique adaptive through the analysis of natural scene features, allowing the camera parameters to hold accurate throughout the acquisition session in the presence of parameter drift.
Motion compensated image interpolation

Field skipping is a variable technique for reducing drastically the bit rate necessary to transmit a television signal. All fields have to be reconstructed at the receiver end, but nearest-neighbor or linear interpolations give poor performances when significant scene activity is present; therefore, some motion compensation scheme is mandatory. Although any of the algorithms proposed for motion estimation can be used for motion compensated interpolation, in this paper only the pel-recursive algorithm is considered. Past experience is briefly summarized and, based on criticism, improvements to the basic algorithm are proposed that seem to lead to an estimated motion field that is accurate enough. Multiple recursions and appropriate selection rules are proposed to counteract streaking effects of the recursive algorithm when it crosses image boundaries. The estimation of a forward and a backward motion field is used to identify image areas where occlusion effects are present. Experimental results are presented that seem to indicate that the proposed interpolation scheme has good performance.
Demosaicing strategy identification via eigenalgorithms

The identification of the camera that has acquired a specific image can be performed via several device-related footprints. Among these, it is possible to look for the traces left by the adopted color demosaicing strategy, which varies according to the camera model and vendor. The paper presents an identification strategy that re-processes the analyzed image with a set of distinctive CFA interpolation algorithms (eigenalgorithms) and, according to the correlation of the output with the original image, builds a set of features that permits identifying the algorithm. The proposed solution performs well with respect to other state-of-the-art solutions also when the analyzed image is severely compressed.
A compressive-sensing based watermarking scheme for sparse image tampering identification

In this paper we describe a robust watermarking scheme for image tampering identification and localization. A compact representation of the image is first produced by assembling a feature vector consisting of pseudo-random projections of the decimated image. Then, the quantized projections are encoded to form a hash, which is robustly embedded as a watermark in the image. By recovering the watermark the random projections are obtained, and then used to estimate the distortion of the received image. If tampering is sufficiently sparse or compressible in some basis description, a map of the introduced modification is recovered. The system relies on Compressive Sensing and Distributed Source Coding principles to reduce the size of the hash of a 1024 × 1024 image, to about 4,000 bits. With this hash length, tampering sparse up to 20% and with a tampering energy around a PSNR of 15 dB can be successfully localized.
Intra Mode Decision Based on Spatio-Temporal Cues in Pixel Domain Wyner-ZIV Video Coding

Distributed source coding principles have been recently applied to video coding in order to achieve a flexible distribution of the complexity burden between the encoder and the decoder. In this paper we elaborate on a pixel based Wyner-Ziv video codec that shifts all the complexity of the motion estimation phase to the decoder, thus achieving light encoding. We observe that the correlation noise statistics describing the relationship between the frame to be encoded and the side information available at the decoder is not spatially stationary. For this reason we introduce a mode decision scheme either at the encoder or at the decoder in such a way that when the estimated correlation is weak we opt for intra coding on a block-by-block basis. Both spatial and temporal criteria are used to determine whether a block is better intra coded or not
3D correspondences in textured depth-maps through planar similarity transform

Many low cost realtime depth-map reconstruction devices recently appeared on the market opened new opportunities for the Computer Vision community to integrate these information in many research areas. The knowledge of the underlying depth-map together with a visual snapshot of the scene can greatly improve the robustness of points matching between different views even for wide baseline acquisitions. In this paper we are presenting how visual correspondences from different views can be identified by robust Similarity Invariant Descriptors (SID) once their laying plane is known. Furthermore depth-map, providing with a rough geometrical description of the underlying scene, allows to select only feature points belonging to almost planar regions, skipping geometrical corners or edges that undergo non-linear distortion for viewpoint changes. The proposed SIDs keep much more information of the original area with respect to commonly used affine invariant descriptors, like SIFT of GLOH, making the proposed approach much less prone to false matches even for wide viewpoint changes.
A reduced-reference video structural similarity metric based on no-reference estimation of channel-induced distortion

The reduced-reference (RR) approximation of a full-reference (FR) video quality assessment method is a convenient way to build evaluation metrics which are both intrinsically well correlated with human judgments and feasible to implement in a network scenario, without the need to explore the perceptual significance of new video features through mean opinion score tests. In this paper, we propose a RR approximation of the video structural similarity index (VSSIM), a FR metric which is known to be well descriptive of the video quality perceived by users. We focus on the visual degradation produced by channel transmission errors: first, at the encoder, a small set of salient structural video features is assembled and transmitted through the RR channel to the end-user; then, at the decoder the feature vector is combined with a fine-granularity, no-reference estimate of the channel-induced distortion to produce the VSSIM approximation. By uniformly quantizing the feature vector and compressing it using a context-adaptive, variable length encoder, we show that good correlation coefficients with ground-truth VSSIM (ρ = 0.85) may be achieved spending, respectively, less than 12 and 27 kbps for a video sequence with CIF or SD resolution.
Object-based sound synthesis for virtual environments-using musical acoustics

We show how to construct an object-based environment for sound generation, whose objects can be individually synthesized and which can interact with each other through modelling of a potential interaction topology. We show how this interaction topology can be mode dynamic and time-varying. We also discuss how we envision an object-based environment that integrates geometric, radiometric, and intrinsic/extrinsic acoustic properties. We finally illustrate our first results towards modelling of complex sound generation systems.
Self-calibration of two microphone arrays from volumetric acoustic maps in non-reverberant rooms

In this paper we present a methodology for the self-calibration of two microphone arrays based on the localization of acoustic sources from volumetric acoustic maps, one for each array. A set of correspondences are obtained moving the acoustic source at different locations in space. The proposed algorithm estimates the rigid motion that brings the coordinate system of the second microphone array to the first one through the solution of a least squares problem. The approach presented here enables the self-calibration even when the acoustic sources are in the near-field of the microphone arrays, thus extending the methodology presented by the authors in another work.
Using partial information for face recognition and pose estimation

The main achievement of this work is the development of a new face recognition approach called partial principal component analysis (P/sup 2/CA), which exploits the novel concept of using only partial information for the recognition stage. This approach uses 3D data in the training stage but it permits to use either 2D or 3D data in the recognition stage, making the whole system more flexible. Preliminary experiments carried out on a multi-view face database composed of 18 individuals have shown robustness against big pose variations obtaining higher recognition rates than the conventional PCA method. Moreover, the P/sup 2/CA method can estimate the pose of the face under different illuminations with accuracy of the 96.15% when classifying the face images in 0/spl deg/, /spl plusmn/30/spl deg/, /spl plusmn/45/spl deg/, /spl plusmn/60/spl deg/ and /spl plusmn/90/spl deg/ views.