PRNU based source camera attribution for image sets anonymized with patch-match algorithm

Patch-Match is an efficient algorithm used for structural image editing and available as a tool on popular commercial photo-editing software. The tool allows users to insert or remove objects from photos using information from similar scene content. Recently, a modified version of this algorithm was proposed as a counter-measure against Photo-Response Non-Uniformity (PRNU) based Source Camera Identification (SCI). The algorithm can provide anonymity at a great rate (97%) and impede PRNU based SCI without the need of any other information, hence leaving no-known recourse for the PRNU-based SCI. In this paper, we propose a method to identify sources of the Patch-Match-applied images by using randomized subsets of images and the traditional PRNU based SCI methods. We evaluate the proposed method on two forensics scenarios in which an adversary makes use of the Patch-Match algorithm and distorts the PRNU noise pattern in the incriminating images she took with his camera. Our results show that it is possible to link sets of Patch-Match-applied images back to their source camera even in the presence of images that come from unknown cameras. To our best knowledge, the proposed method represents the very first counter-measure against the usage of Patch-Match in the digital forensics literature.     

Using anisotropic diffusion for efficient extraction of sensor noise in camera identification

Each digital camera has an intrinsic fingerprint that is unique to each camera. This device fingerprint can be extracted from an image and can be compared with a reference device fingerprint to determine the device origin. The complexity of the filters proposed to accomplish this is increasing. In this note, we use a relatively simple algorithm to extract the sensor noise from images. It has the advantages of being easy to implement and parallelize, and working faster than the wavelet filter that is common for this application. In addition, we compare the performance with a simple median filter and assess whether a previously proposed fingerprint enhancement technique improves results. Experiments are performed on approximately 7500 images originating from 69 cameras, and the results are compared with this often used wavelet filter. Despite the simplicity of the proposed method, the performance exceeds the common wavelet filter and reduces the time needed for the extraction.     

A local variance based approach to alleviate the scene content interference for source camera identification

Identifying the source camera of images is becoming increasingly important nowadays. A popular approach is to use a type of pattern noise called photo-response non-uniformity (PRNU). The noise of image contains the patterns which can be used as a fingerprint. Despite that, the PRNU-based approach is sensitive towards scene content and image intensity. The identification is poor in areas having low or saturated intensity, or in areas with complicated texture. The reliability of different regions is difficult to model in that it depends on the interaction of scene content and the characteristics of the denoising filter used to extract the noise. In this paper, we showed that the local variance of the noise residual can measure the reliability of the pixel for PRNU-based source camera identification. Hence, we proposed to use local variance to characterize the severeness of the scene content artifacts. The local variance is then incorporated to the general matched filter and peak to correlation energy (PCE) detector to provide an optimal framework for signal detection. The proposed method is tested against several state-of-art methods. The experimental results show that the local variance based approach outperformed other state-of-the-art methods in terms of identification accuracy.     

Robust Shoeprint Retrieval Method Based on Local‐to‐Global Feature Matching for Real Crime Scenes

In this study, an automatic and robust crime scene shoeprint retrieval method is proposed. As most shoeprints left at crime scenes are randomly partial and noisy, crime scene shoeprint retrieval is a challenging task. To handle partial, noisy shoeprint images, we employ denoising deep belief network (DBN) to extract local features and use spatial pyramid matching (SPM) to obtain a local‐to‐global matching score. In this study, 536 query shoeprint images from crime scenes and a large scale database containing 34,768 shoeprint images are used to evaluate the retrieval performance. Experimental results show that the proposed method outperforms other state‐of‐the‐art methods in terms of retrieval accuracy, feature dimension, and retrieval speed. The proposed method achieves a cumulative match score (CMS) of 65.67% at top 10 which is 5.60% higher than the second best performing method.     

Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching

Copy-move is one of the most commonly used image tampering operation, where a part of image content is copied and then pasted to another part of the same image. In order to make the forgery visually convincing and conceal its trace, the copied part may subject to post-processing operations such as rotation and blur. In this paper, we propose a polar cosine transform and approximate nearest neighbor searching based copy-move forgery detection algorithm. The algorithm starts by dividing the image into overlapping patches. Robust and compact features are extracted from patches by taking advantage of the rotationally-invariant and orthogonal properties of the polar cosine transform. Potential copy-move pairs are then detected by identifying the patches with similar features, which is formulated as approximate nearest neighbor searching and accomplished by means of locality-sensitive hashing (LSH). Finally, post-verifications are performed on potential pairs to filter out false matches and improve the accuracy of forgery detection. To sum up, the LSH based similar patch identification and the post-verification methods are two major novelties of the proposed work. Experimental results reveal that the proposed work can produce accurate detection results, and it exhibits high robustness to various post-processing operations. In addition, the LSH based similar patch detection scheme is much more effective than the widely used lexicographical sorting.     

A complete passive blind image copy-move forensics scheme based on compound statistics features

Since most sensor pattern noise based image copy-move forensics methods require a known reference sensor pattern noise, it generally results in non-blinded passive forensics, which significantly confines the application circumstances. In view of this, a novel passive-blind image copy-move forensics scheme is proposed in this paper. Firstly, a color image is transformed into a grayscale one, and wavelet transform based de-noising filter is used to extract the sensor pattern noise, then the variance of the pattern noise, the signal noise ratio between the de-noised image and the pattern noise, the information entropy and the average energy gradient of the original grayscale image are chosen as features, non-overlapping sliding window operations are done to the images to divide them into different sub-blocks. Finally, the tampered areas are detected by analyzing the correlation of the features between the sub-blocks and the whole image. Experimental results and analysis show that the proposed scheme is completely passive-blind, has a good detection rate, and is robust against JPEG compression, noise, rotation, scaling and blurring.     

Single‐Image Rectification Technique in Forensic Science

Many researchers have been working in Spain to document the communal graves of those assassinated during the Spanish Civil War. This article shows the results obtained with two low‐cost photogrammetric techniques for the basic documentation of forensic studies. These low‐cost techniques are based on single‐image rectification and the correction of the original photo displacement due to the projection and perspective distortions introduced by the lens of the camera. The capability of image rectification is tested in an excavation in the village of Loma de Montija (Burgos, Spain). The results of both techniques are compared with the more accurate data obtained from a laser scanner system RIEGL LMS‐Z390i to evaluate the error in the lengths. The first technique uses a camera situated on a triangle‐shaped pole at a height of 5 m and the second positions the camera over the grave using a linearly actuated device. The first technique shows measurement errors less than 6%, whereas the second shows greater errors (between 8% and 14%) owing to the positioning of the carbon‐fiber cross on an uneven surface.     

A simple and effective image-statistics-based approach to detecting recaptured images from LCD screens

It is now extremely easy to recapture high-resolution and high-quality images from LCD (Liquid Crystal Display) screens. Recaptured image detection is an important digital forensic problem, as image recapture is often involved in the creation of a fake image in an attempt to increase its visual plausibility. State-of-the-art image recapture forensic methods make use of strong prior knowledge about the recapturing process and are based on either the combination of a group of ad-hoc features or a specific and somehow complicated dictionary learning procedure. By contrast, we propose a conceptually simple yet effective method for recaptured image detection which is built upon simple image statistics and a very loose assumption about the recapturing process. The adopted features are pixel-wise correlation coefficients in image differential domains. Experimental results on two large databases of high-resolution, high-quality recaptured images and comparisons with existing methods demonstrate the forensic accuracy and the computational efficiency of the proposed method.     

A New Anti‐forensic Scheme—Hiding the Single JPEG Compression Trace for Digital Image

To prevent image forgeries, a number of forensic techniques for digital image have been developed that can detect an image's origin, trace its processing history, and can also locate the position of tampering. Especially, the statistical footprint left by JPEG compression operation can be a valuable source of information for the forensic analyst, and some image forensic algorithm have been raised based on the image statistics in the DCT domain. Recently, it has been shown that footprints can be removed by adding a suitable anti‐forensic dithering signal to the image in the DCT domain, this results in invalid for some image forensic algorithms. In this paper, a novel anti‐forensic algorithm is proposed, which is capable of concealing the quantization artifacts that left in the single JPEG compressed image. In the scheme, a chaos‐based dither is added to an image's DCT coefficients to remove such artifacts. Effectiveness of both the scheme and the loss of image quality are evaluated through the experiments. The simulation results show that the proposed anti‐forensic scheme can verify the reliability of the JPEG forensic tools.     

Efficient height measurement method of surveillance camera image

As surveillance cameras are increasingly installed, their films are often submitted as evidence of crime, but very scant detailed information such as features and clothes is obtained due to the limited camera performance. Height, however, is relatively not significantly influenced by the camera performance. This paper studied the height measurement method using images from a CCTV. The information on the height was obtained via photogrammetry, including the reference points in the photographed area and the calculation of the relationship between a 3D space and a 2D image through linear and nonlinear calibration. Using this correlation, this paper suggested the height measurement method, which projects a 3D virtual ruler onto the image. This method has been proven to offer more stable values within the range of data convergence than those of other existing methods.     

数码翻拍的图像质量分析及应用标准化探讨

翻拍是司去鉴定中固定平面类证据的常用方法之一。数码翻拍突破了传统翻拍的界限。提供了高效方法。但其图像质量及应用标准化研究尚未形成体系。本文以数码相机为例，深入分析影响翻拍质量的各因素，并对其在司法实践中如何保证和提高翻拍质量进行技术方法上的探讨。通过分析研究。界定了数码翻拍中器材使用标准、拍摄条件标准及操作标准。为司法鉴定中的翻拍应用标准化提供了借鉴与参考。     

A Multimodal Fusion Approach for Bullet Identification Systems

In the field of forensic science, bullet identification is based on the fact that firing the cartridge from a barrel leaves exclusive microscopic striation on the fired bullets as the fingerprint of the firearm. The bullet identification methods are categorized in 2‐D and 3‐D based on their image acquisition techniques. In this study, we focus on 2‐D optical images using a multimodal technique and propose several distinct methods as its modalities. The proposed method uses a multimodal rule‐based linear weighted fusion approach which combines the semantic level decisions from different modalities with a linear technique that its optimized modalities weights have been identified by the genetic algorithm. The proposed approach was applied on a dataset, which includes 180 2‐D bullet images fired from 90 different AK‐47 barrels. The experimentations showed that our approach attained better results compared to common methods in the field of bullet identification.     

Digital camera identification using PRNU: A feature based approach

Source camera identification is one of the emerging field in digital image forensics, which aims at identifying the source camera used for capturing the given image. The technique uses photo response non-uniformity (PRNU) noise as a camera fingerprint, as it is found to be one of the unique characteristic which is capable of distinguishing the images even if they are captured from similar cameras. Most of the existing PRNU based approaches are very sensitive to the random noise components existing in the estimated PRNU, and also they are not robust when some simple manipulations are performed on the images. Hence a new feature based approach of PRNU is proposed for the source camera identification by choosing the features which are robust for image manipulations. The PRNU noise is extracted from the images using wavelet based denoising method and is represented by higher order wavelet statistics (HOWS), which are invariant features for image manipulations and geometric variations. The features are fed to support vector machine classifiers to identify the originating source camera for the given image and the results have been verified by performing ten-fold cross validation technique. The experiments have been carried out using the images captured from various cell phone cameras and it demonstrated that the proposed algorithm is capable of identifying the source camera of the given image with good accuracy. The developed technique can be used for differentiating the images, even if they are captured from similar cameras, which belongs to same make and model. The analysis have also showed that the proposed technique remains robust even if the images are subjected to simple manipulations or geometric variations.     

Identification of Natural Images and Computer‐Generated Graphics Based on Statistical and Textural Features

To discriminate the acquisition pipelines of digital images, a novel scheme for the identification of natural images and computer‐generated graphics is proposed based on statistical and textural features. First, the differences between them are investigated from the view of statistics and texture, and 31 dimensions of feature are acquired for identification. Then, LIBSVM is used for the classification. Finally, the experimental results are presented. The results show that it can achieve an identification accuracy of 97.89% for computer‐generated graphics, and an identification accuracy of 97.75% for natural images. The analyses also demonstrate the proposed method has excellent performance, compared with some existing methods based only on statistical features or other features. The method has a great potential to be implemented for the identification of natural images and computer‐generated graphics.     

A Novel Method for the Photographic Recovery of Fingermark Impressions from Ammunition Cases Using Digital Imaging

The photographic preservation of fingermark impression evidence found on ammunition cases remains problematic due to the cylindrical shape of the deposition substrate preventing complete capture of the impression in a single image. A novel method was developed for the photographic recovery of fingermarks from curved surfaces using digital imaging. The process involves the digital construction of a complete impression image made from several different images captured from multiple camera perspectives. Fingermark impressions deposited onto 9‐mm and 0.22‐caliber brass cartridge cases and a plastic 12‐gauge shotgun shell were tested using various image parameters, including digital stitching method, number of images per 360° rotation of shell, image cropping, and overlap. The results suggest that this method may be successfully used to recover fingermark impression evidence from the surfaces of ammunition cases or other similar cylindrical surfaces.     

The Decipherment of Latent White Board Entries

This case involves an incriminating message written on a white board and erased before the investigators seized the evidence. Investigators inquired about the possibility of recovering the entries and subsequently submitted the board to the Documents and Digital Evidence Branch for examination. In an attempt to recover latent handwritten entries, examinations were conducted utilizing the Foster and Freeman Video Spectral Comparator 6000/Hyper Spectral (VSC 6000/HS). Latent handwritten entries were observed with the use of an excitation band‐pass filter in the range of 485 nm‐610 nm with a longpass barrier filter at 645 nm in front of the camera lens of the VSC 6000/HS. Entries were also recovered and imaged using an alternate light source with the Phase One (P65) imaging system. Multiple latent handwritten entries were recovered utilizing various instruments and lighting techniques. This report discusses the details concerning the examination and the results.     

图像降噪和增强对人脸识别系统识别性能的影响

目的研究图像降噪和图像增强方法对人脸识别系统识别性能的影响,以期为人脸识别系统应用过程中的图像处理方法选取提供理论指导和技术方案。方法收集33起人像鉴定领域实际案例中的人脸图像素材,研究以高斯滤波和小波变换为代表的图像降噪技术以及具有边缘保持和小波变换特性的单帧图像超分辨率增强技术对人脸识别系统识别性能的影响,并对不同图像处理方法对人脸识别性能的影响进行量化比较分析。结果本文研究的图像降噪技术均显著提高了人脸识别系统的识别准确性,而图像增强技术虽然提高了人脸图像显示效果,但对人脸识别系统的识别性能无正向促进作用。此外,高斯模糊图像处理的图像降噪方法虽然简单,但与本文研究的其他方法比较,其在人脸识别系统识别性能改善方面效果最显著。结论人脸图像质量对人脸识别系统的识别性能具有显著影响,可以通过图像处理技术改善人脸图像质量进而提高人脸识别系统的识别准确性。其中,图像降噪处理可以显著提高人脸识别系统的识别性能,且比图像增强技术更适合于实际人像鉴定应用中的人脸识别系统识别性能增强。     

Differentiating synthetic and optical zooming for passive video forgery detection: An anti-forensic perspective

A video can be manipulated using synthetic zooming without using the state-of-the-art video forgeries. Synthetic zooming is performed by upscaling individual frames of a video with varying scale factors followed by cropping them to the original frame size. These manipulated frames resemble genuine natural (optical) camera zoomed frames and hence may be misclassified as a pristine video by video forgery detection algorithms. Even if such a video is classified as forged, forensic investigators may ignore the results, believing it as part of an optical camera zooming activity. Hence, this can be used as an anti-forensic method which eliminates digital evidence. In this paper, we propose a method for differentiating optical camera zooming from synthetic zooming for video tampering detection. The features used for this method are pixel variance correlation and sensor pattern noise. Experimental results on a dataset containing 3200 videos show the effectiveness of the proposed method.     

Validation of image stream hashing: A forensic method for content verification

How do forensic examiners know if they have altered an image stream when converting a digital image from one codec or file container to another for analysis? Forensic standards and best practices recommend avoiding alteration or degradation of multimedia data during transcoding. An image stream hashing method was recently introduced to the forensic science community to answer the question above. This paper offers an initial validation study of image stream hashing method that may answer the question above. The first half of the study's experiments tested the image stream hashing method to measure fitness for use in forensic science while identifying errors and limitations. The study's second phase analyzed the systematic errors detected in initial tests to discover error causation. Causation analysis identified four method limitations subsequently used to develop proposed standard controls of method operations. The final study phase repeated the initial experiments used in the first phase while implementing the proposed standard controls of method operations. Initial test results indicated the method had significant error rates, limiting the effectiveness of the method to only three of the five file types used in the study. The final testing phase revealed that implementing proposed standard controls of method operations reduced the potential systematic errors to a negligible level when using the image stream hashing method for content verification. The validation study concluded that examiners could use the image stream hashing method for forensic science only by implementing error mitigation techniques that utilize the proposed standard controls of method operations.