Christian Riess

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features Sift and Surf, as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.

Exposing Digital Image Forgeries by Illumination Color Classification

For decades, photographs have been used to document space-time events and they have often served as evidence in courts. Although photographers are able to create composites of analog pictures, this process is very time consuming and requires expert knowledge. Today, however, powerful digital image editing software makes image modifications straightforward. This undermines our trust in photographs and, in particular, questions pictures as evidence for real-world events. In this paper, we analyze one of the most common forms of photographic manipulation, known as image composition or splicing. We propose a forgery detection method that exploits subtle inconsistencies in the color of the illumination of images. Our approach is machine-learning-based and requires minimal user interaction. The technique is applicable to images containing two or more people and requires no expert interaction for the tampering decision. To achieve this, we incorporate information from physics- and statistical-based illuminant estimators on image regions of similar material. From these illuminant estimates, we extract texture- and edge-based features which are then provided to a machine-learning approach for automatic decision-making. The classification performance using an SVM meta-fusion classifier is promising. It yields detection rates of 86% on a new benchmark dataset consisting of 200 images, and 83% on 50 images that were collected from the Internet.

CONRAD--a software framework for cone-beam imaging in radiology.

PURPOSE In the community of x-ray imaging, there is a multitude of tools and applications that are used in scientific practice. Many of these tools are proprietary and can only be used within a certain lab. Often the same algorithm is implemented multiple times by different groups in order to enable comparison. In an effort to tackle this problem, the authors created CONRAD, a software framework that provides many of the tools that are required to simulate basic processes in x-ray imaging and perform image reconstruction with consideration of nonlinear physical effects. METHODS CONRAD is a Java-based state-of-the-art software platform with extensive documentation. It is based on platform-independent technologies. Special libraries offer access to hardware acceleration such as OpenCL. There is an easy-to-use interface for parallel processing. The software package includes different simulation tools that are able to generate up to 4D projection and volume data and respective vector motion fields. Well known reconstruction algorithms such as FBP, DBP, and ART are included. All algorithms in the package are referenced to a scientific source. RESULTS A total of 13 different phantoms and 30 processing steps have already been integrated into the platform at the time of writing. The platform comprises 74.000 nonblank lines of code out of which 19% are used for documentation. The software package is available for download at http://conrad.stanford.edu. To demonstrate the use of the package, the authors reconstructed images from two different scanners, a table top system and a clinical C-arm system. Runtimes were evaluated using the RabbitCT platform and demonstrate state-of-the-art runtimes with 2.5 s for the 256 problem size and 12.4 s for the 512 problem size. CONCLUSIONS As a common software framework, CONRAD enables the medical physics community to share algorithms and develop new ideas. In particular this offers new opportunities for scientific collaboration and quantitative performance comparison between the methods of different groups.

Scene illumination as an indicator of image manipulation

The goal of blind image forensics is to distinguish original and manipulated images. We propose illumination color as a new indicator for the assessment of image authenticity. Many images exhibit a combination of multiple illuminants (flash photography, mixture of indoor and outdoor lighting, etc.). In the proposed method, the user selects illuminated areas for further investigation. The illuminant colors are locally estimated, effectively decomposing the scene in a map of differently illuminated regions. Inconsistencies in such a map suggest possible image tampering. Our method is physics-based, which implies that the outcome of the estimation can be further constrained if additional knowledge on the scene is available. Experiments show that these illumination maps provide a useful and very general forensics tool for the analysis of color images.

On rotation invariance in copy-move forgery detection

The goal of copy-move forgery detection is to find duplicated regions within the same image. Copy-move detection algorithms operate roughly as follows: extract blockwise feature vectors, find similar feature vectors, and select feature pairs that share highly similar shift vectors. This selection plays an important role in the suppression of false matches. However, when the copied region is additionally rotated or scaled, shift vectors are no longer the most appropriate selection technique. In this paper, we present a rotation-invariant selection method, which we call Same Affine Transformation Selection (SATS). It shares the benefits of the shift vectors at an only slightly increased computational cost. As a byproduct, the proposed method explicitly recovers the parameters of the affine transformation applied to the copied region. We evaluate our approach on three recently proposed feature sets. Our experiments on ground truth data show that SATS outperforms shift vectors when the copied region is rotated, independent of the size of the image.

Color constancy and non-uniform illumination: Can existing algorithms work?

The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.

A Comparative Error Analysis of Current Time-of-Flight Sensors

Time-of-flight (ToF) cameras suffer from systematic errors, which can be an issue in many application scenarios. In this paper, we investigate the error characteristics of eight different ToF cameras. Our survey covers both well established and recent cameras including the Microsoft Kinect V2. We present up to six experiments for each camera to quantify different types of errors. For each experiment, we outline the basic setup, present comparable data for each camera, and discuss the respective results. The results discussed in this paper enable the community to make appropriate decisions in choosing the best matching camera for a certain application. This work also lays the foundation for a framework to benchmark future ToF cameras. Furthermore, our results demonstrate the necessity for correcting characteristic measurement errors. We believe that the presented findings will allow 1) the development of novel correction methods for specific errors and 2) the development of general data processing algorithms that are able to robustly operate on a wider range of cameras and scenes.

Multi-Illuminant Estimation With Conditional Random Fields

Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.

Reconstruction of scalar and vectorial components in X-ray dark-field tomography

Significance X-ray Talbot−Lau grating interferometry provides a differential phase contrast and a dark-field image containing scattering information. The dark-field image is sensitive to granular and fibrous microstructures with sizes in the range of the grating periods (circa 5 μm), much below the typical resolution of medical imaging techniques like angiography or fluoroscopy (circa 150 μm). Dark-field contrast is influenced by the orientation of the microstructure in the object. We present an approach to recover the local microstructure orientation in a tomographic 3D reconstruction. Per voxel, we quantitatively reconstruct the vector of the dominant local orientation and the amount of (an)isotropic scattering for relatively large samples using a standard medical X-ray setup. This is experimentally shown for various specimens exhibiting varying degrees of structural orderings. Grating-based X-ray dark-field imaging is a novel technique for obtaining image contrast for object structures at size scales below setup resolution. Such an approach appears particularly beneficial for medical imaging and nondestructive testing. It has already been shown that the dark-field signal depends on the direction of observation. However, up to now, algorithms for fully recovering the orientation dependence in a tomographic volume are still unexplored. In this publication, we propose a reconstruction method for grating-based X-ray dark-field tomography, which models the orientation-dependent signal as an additional observable from a standard tomographic scan. In detail, we extend the tomographic volume to a tensorial set of voxel data, containing the local orientation and contributions to dark-field scattering. In our experiments, we present the first results of several test specimens exhibiting a heterogeneous composition in microstructure, which demonstrates the diagnostic potential of the method.

Automated Image Forgery Detection through Classification of JPEG Ghosts

We present a method for automating the detection of the so-called JPEG ghost s. JPEG ghost s can be used for discriminating single- and double JPEG compression, which is a common cue for image manipulation detection. The JPEG ghost scheme is particularly well-suited for non-technical experts, but the manual search for such ghost s can be both tedious and error-prone. In this paper, we propose a method that automatically and efficiently discriminates single- and double-compressed regions based on the JPEG ghost principle. Experiments show that the detection results are highly competitive with state-of-the-art methods, for both, aligned and shifted JPEG grids in double-JPEG compression.