Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation

The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to generation of videos remain largely untackled. With this perspective, this work introduces methods to mitigate disruptive effects of widely deployed H.264 and H.265 video compression standards on PRNU estimation. Our approach involves an intervention in the decoding process to eliminate a filtering procedure applied at the decoder to reduce blockiness. It also utilizes decoding parameters to develop a weighting scheme and adjust the contribution of video frames at the macroblock level to PRNU estimation process. Results obtained on videos captured by 28 cameras show that our approach increases the PRNU matching metric up to more than five times over the conventional estimation method tailored for photos

A preliminary examination technique for audio evidence to distinguish speech from non-speech using objective speech quality measures

Forensic practitioners are faced more and more with large volumes of data. Therefore, there is a growing need for computational techniques to aid in evidence collection and analysis. With this study, we introduce a technique for preliminary analysis of audio evidence to discriminate between speech and non-speech. The novelty of our approach lies in the use of well-established speech quality measures for characterizing speech signals. These measures rely on models of human perception of speech to provide objective and reliable measurements of changes in characteristics that influence speech quality. We utilize this capability to compute quality scores between an audio and its noise-suppressed version and to model variations of these scores in speech as compared to those in non-speech audio. Tests performed on 11 datasets with widely varying characteristics show that the technique has a high discrimination capability, achieving an identification accuracy of 96 to 99% in most test cases, and offers good generalization properties across different datasets. Results also reveal that the technique is robust against encoding at low bit-rates, application of audio effects and degradations due to varying degrees of background noise. Performance comparisons made with existing studies show that the proposed method improves the state-of-the-art in audio content identification. (C) 2014 Elsevier B.V. All rights reserved

Carving Orphaned JPEG File Fragments

File carving techniques allow for recovery of files from storage devices in the absence of any file system metadata. When data are encoded and compressed, the current paradigm of carving requires the knowledge of the compression and encoding settings to succeed. In this paper, we advance the state of the art in JPEG file carving by introducing the ability to recover fragments of a JPEG file when the associated file header is missing. To realize this, we examined JPEG file headers of a large number of images collected from Flickr photo sharing site to identify their structural characteristics. Our carving approach utilizes this information in a new technique that performs two tasks. First, it decompresses the incomplete file data to obtain a spatial domain representation. Second, it determines the spatial domain parameters to produce a perceptually meaningful image. Recovery results on a variety of JPEG file fragments show that given the knowledge of Huffman code tables, our technique can very reliably identify the remaining decoder settings for all fragments of size 4 KiB or above. Although errors due to detection of image width, placement of image blocks, and color and brightness adjustments can occur, these errors reduce significantly when fragment sizes are >32 KiB

Source camera identification based on CFA interpolation

In this work, we focus our interest on blind source camera identification problem by extending our results in the direction of. The interpolation in the color surface of an image due to the use of a color filter array (CFA) forms the basis of the paper. We propose to identify the source camera of an image based on traces of the proprietary interpolation algorithm deployed by a digital camera. For this purpose, a set of image characteristics are defined and then used in conjunction with a support vector machine based multi-class classifier to determine the originating digital camera. We also provide initial results on identifying source among two and three digital cameras

Analysis of seam-carving-based anonymization of images against PRNU noise pattern-based source attribution

The availability of sophisticated source attribution techniques raises new concerns about privacy and anonymity of photographers, activists, and human right defenders who need to stay anonymous while spreading their images and videos. Recently, the use of seam-carving, a content-aware resizing method, has been proposed to anonymize the source camera of images against the well-known photoresponse nonuniformity (PRNU)-based source attribution technique. In this paper, we provide an analysis of the seam-carving-based source camera anonymization method by determining the limits of its performance introducing two adversarial models. Our analysis shows that the effectiveness of the deanonymization attacks depend on various factors that include the parameters of the seam-carving method, strength of the PRNU noise pattern of the camera, and an adversary's ability to identify uncarved image blocks in a seam-carved image. Our results show that, for the general case, there should not be many uncarved blocks larger than the size of 50x50 pixels for successful anonymization of the source camera.Center for Interdisciplinary Studies in Security and Privacy, New York University Abu Dhabi, Abu Dhabi, United Arab Emirate

Sensor Fingerprint Identification Through Composite Fingerprints and Group Testing

The photo response non-uniformity noise associated with an imaging sensor has been shown to be a unique and persistent identifier that can be treated as the sensor's digital fingerprint. The method for attributing an image to a particular camera, however, is not suitable for source identification due to efficiency considerations, which is a one-to-many matching of a single fingerprint against a database of fingerprints. To address this problem, we propose a group-testing approach based on the notion of composite fingerprints (CFs), generated by combining many actual fingerprints together into a single fingerprint. Our technique organizes a database of fingerprints into an unordered binary search tree, wherein each internal node is represented by a fingerprint composited from all the fingerprints at the leaf nodes in the subtree beneath that node. Different search strategies are considered, and the performance is analyzed analytically and verified using numerical simulations as well as experimental results. Our results are presented in comparison with the linear search-based approach that utilizes fingerprint digests for more effective computation. Results obtained under the best achievable accuracy showed that the proposed method yields a lower overall computational cost. It is also shown that by complementary use of the fingerprint dimension reduction and CF-based search tree approaches, it is possible to further improve the search efficiency

Extracting PRNu noise from H.264 coded videos

26th European Signal Processing Conference (2018 : Rome; Italy)Every device equipped with a digital camera has a unique identity. This phenomenon is essentially due to a systematic noise component of an imaging sensor, known as photo-response non-uniformity (PRNU) noise. An imaging sensor inadvertently introduces this noise pattern to all media captured by that imaging sensor. The procedure for extracting PRNU noise has been well studied in the context of photographic images, however, its extension to video has so far been neglected. In this work, considering H.264 coding standard, we describe a procedure to extract sensor fingerprint from non-stabilized videos. The crux of our method is to remove a filtering procedure applied at the decoder to reduce blockiness and to use macroblocks selectively when estimating PRNU noise pattern. Results show that our method has a potential to improve matching performance significantly

Towards Measuring Uniqueness of Human Voice

2017 IEEE WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS) (2017 : Rennes; France)The use of voice as a biometric modality for user authentication and identification has grown very rapidly. It is therefore very important that we understand limitations of such systems which will ultimately depend on the discriminative power of the voice biometric. In this paper, we have contributed towards measuring distinctiveness of voice biometric by both formulating a new measure and creating a new dataset to perform more reliable measurements. For this purpose, we evaluate the prominent approaches in the field and propose a new approach that better incorporates within-user variability and is analytically more tractable. Our newly created dataset includes voice samples extracted from close to two thousand TED Talks videos. Overall our measurements on this dataset revealed a biometric information content of about 60 bits in human voice. Further, tests performed by adding some generic voice effects on the samples show that the distinctiveness reduces by almost 20 bits, implying that when true variability is reflected in user samples resulting entropy may further reduce.BİDEB-221

Analysis of Seam-Carving-Based Anonymization of Images Against PRNU Noise Pattern-Based Source Attribution

The availability of sophisticated source attribution techniques raises new concerns about privacy and anonymity of photographers, activists, and human right defenders who need to stay anonymous while spreading their images and videos. Recently, the use of seam-carving, a content-aware resizing method, has been proposed to anonymize the source camera of images against the well-known photoresponse nonuniformity (PRNU)-based source attribution technique. In this paper, we provide an analysis of the seam-carving-based source camera anonymization method by determining the limits of its performance introducing two adversarial models. Our analysis shows that the effectiveness of the deanonymization attacks depend on various factors that include the parameters of the seam-carving method, strength of the PRNU noise pattern of the camera, and an adversary's ability to identify uncarved image blocks in a seam-carved image. Our results show that, for the general case, there should not be many uncarved blocks larger than the size of 50x50 pixels for successful anonymization of the source camera

Fast camera fingerprint matching in very large databases

24th IEEE International Conference on Image Processing (2017 : Beijing; China)Given a query image or video, or a known camera fingerprint, there is a lack of capabilities for fast identification of media, from a large repository of images and videos, that match the query fingerprint. This work introduces a new approach that improves the computation efficiency of pairwise camera fingerprint matching and incorporates group testing to make the search more effective. More specifically, we jointly leverage the individual strengths of composite fingerprints and fingerprint digests in a novel manner and design two methods that are superior to existing approaches. The results show that under very high-performance requirements, where the probability of correct identification is close to one with a false-positive rate of zero, the proposed search methods are 2-8 times faster than the state-of-art search methods.The Institute of Electrical and Electronics Engineers Signal Processing Societ