Multimedia Forensics Research Articles

Hierarchical image resampling detection based on blind deconvolution

Resampling detection is a helpful tool in multimedia forensics; however, it is a challenge task in cases with compression and noisy. In this paper, by modeling the recovery of edited images using an inverse filtering process, we propose a novel resampling detection framework based on blind deconvolution. Different interpolation types in the resampling process can be distinguished by our algorithm, which is significant for practical forensics scenarios. Furthermore, in contrast to traditional resampling detection algorithms, our method can effectively avoid interference caused by JPEG block artifacts. As the experimental results show, our method is more robust than other state-of-the-art approaches in the case of strong JPEG compression and substantial Gaussian noise.

An approach to detecting JPEG down-recompression and seam carving forgery under recompression anti-forensics

In multimedia forensics, the detection of forgery on joint photographic experts group (JPEG) images is an interesting and challenging work. JPEG double compression, one of common operations that may occur in the tampering manipulation, has been widely studied. While the quality of the second compression is higher than the quality of the first compression, most approaches have obtained effective detection results, however, it still falls short of accurately detecting the down-recompression that the second compression quality is lower than the first compression quality.Seam carving was originally designed for content-aware image resizing. While it is widely used in computer vision and multimedia processing for legitimate applications, it is also being used for forgery manipulation. Although several methods have been proposed to detect seam carving-based forgery, to this date, the detection of the seam carving forgery under recompression in JPEG images has not been well explored.To address the highly challenging detection problems described above, we proposed a large feature mining-based approach. Over one hundred thousand features from the spatial domain and from the DCT transform domain are developed. Ensemble learning is used to deal with the high dimensionality and to avoid overfitting that may occur with some traditional learning classifier for the detection. Our study demonstrates the efficacy of proposed approach to discriminating JPEG down-recompression and exposing the seam-carving forgery from the same quality and low quality JPEG recompression. And hence, it fills a gap in image forensics.

Wiener 필터링에 기반하는 센서 패턴 노이즈를 활용한 영상 장치 식별 기술 연구

Multimedia such as image, audio, and video is easy to create and distribute with the advance of IT. Since novice uses them for illegal purposes, multimedia forensics are required to protect contents and block illegal usage. This paper presents a multimedia forensic algorithm for video to identify the device used for acquiring unknown video files. First, the way to calculate a sensor pattern noise using Wiener filter (W-SPN) is presented, which comes from the imperfection of photon detectors against light. Then, the way to identify the device is explained after estimating W-SPNs from the reference device and the unknown video. For the experiment, 30 devices including DSLR, compact camera, smartphone, and camcorder are tested and analyzed quantitatively. Based on the results, the presented algorithm can achieve the 96.0% identification accuracy.

A new Zernike moments based technique for camera identification and forgery detection

In multimedia forensics, it is important to identify those images that were captured by a specific camera from a given set of N data images as well as detecting the tampered region in these images if forged. This paper presents a new technique based on Zernike moments feature extraction for blindly classifying correlated PRNU images as well as locating the tampered regions in image under investigation. The proposed clustering algorithm is based on estimating the Zernike moments and applying a hierarchical clustering for classification. The forgery detection algorithm is based on picking up the peak Euclidean distance between the Zernike moments vector of blocks of the scaled-down forged image and its corresponding ones in the capturing camera PRNU. As Zernike moments are scale and rotational invariant, its feature when computed using scaled-down PRNU images lead to considerable computation time saving. Simulation examples are given to verify the effectiveness of the proposed techniques when compared to other state-of-the-art techniques even in case of very weakly correlated PRNU.

Statistical and deterministic approaches for multimedia forensics

The increasing availability and pervasiveness of multimedia data in our society is before our very eyes.As a result of globalization and worldwide connectivity, people from all over the planet are exchanging constantly increasing amounts of images, videos, audio recordings on a daily basis. Coupled with the easy access to user-friendly editing software, this poses a number of problems related to the reliability and trustworthiness of such content, as well as its potential malevolent use. For this reason, the research field of multimedia forensics focuses on the development of forensic tools for verifying the authenticity of multimedia data. The hypothesis of pristine status of images, videos or audio tracks is called into question and can be rejected if traces of manipulation are detected with a certain degree of confidence. In this framework, studying traces left by any operation that could have been employed to process the data, either for malicious purposes or simply to improve their content or presentation, turns out to be of interest for a comprehensive forensic analysis. The goal of this doctoral study is to contribute to the field of multimedia forensics by exploiting intrinsic statistical and deterministic properties of multimedia data. With this respect, much work has been devoted to the study of JPEG compression traces in digital images, resulting in the development of several innovative approaches. Indeed, some of the main related research problems have been addressed and solution based on statistical properties of digital images have been proposed. In particular, the problem of identifying traces of JPEG compressions in images that have been decompressed and saved in uncompressed formats has been extensively studied, resulting in the design of novel statistical detectors. Given the enormous practical relevance, digital images in JPEG formats have also been considered. A novel method aimed at discriminating images compressed only once and more than once has been developed, and tested on a variety of images and forensic scenarios. Being the potential presence of intelligent counterfeiters ever increasingly studied, innovative counterforensic techniques to JPEG compression based on smart reconstruction strategies are proposed.Finally, we explore the possibility of defining and exploiting deterministic properties related to a certain processing operation in the forensic analysis. With this respect, we present a first approach targeted to the detection in one-dimensional data of a common data smoothing operation, the median filter. A peculiarity of this method is the ability of providing a deterministic response on the presence of median filtering traces in the data under investigation.

Automatic Detection of Object-Based Forgery in Advanced Video

Passive multimedia forensics has become an active topic in recent years. However, less attention has been paid to video forensics. Research on video forensics, and especially on automatic detection of object-based video forgery, is still in its infancy. In this paper, we develop an approach for automatic identification and forged segment localization of object-based forged video encoded with advanced frameworks. The proposed approach starts with a frame manipulation detector. An automatic algorithm is proposed to identify object-based video forgery based on the frame manipulation detector. Then, a two-stage automatic algorithm is provided to accurately locate the forged video segments in the suspicious video. To construct the proposed frame manipulation detector, motion residuals are generated from the target video frame sequence. We regard the object-based forgery in video frames as image tampering in the motion residuals and employ the feature extractors that are originally built for still image steganalysis to extract forensic features from the motion residuals. The experiments show that the proposed approach achieves excellent results in both forged video identification and automatic forged temporal segment localization.

Audio Postprocessing Detection Based on Amplitude Cooccurrence Vector Feature

Authentication of audio signals is an important problem in multimedia forensics. Tampering is typically followed by postprocessing that aims to remove the traces of the forgery. The variety of possible postprocessing operations makes tampering detection even more challenging. In this letter, we propose the amplitude cooccurrence vector features, which exploit cooccurrence patterns in audio signals. Experimental results show that our proposed features are able to distinguish between the original audio and the postprocessed audio with an average accuracy of above 95%. Furthermore, it can also effectively discriminate different kinds of postprocessing operations.

ACE–An Effective Anti-forensic Contrast Enhancement Technique

Detecting Contrast Enhancement (CE) in images and anti-forensic approaches against such detectors have gained much attention in multimedia forensics lately. Several contrast enhancement detectors analyze the first order statistics such as gray-level histogram of images to determine whether an image is CE or not. In order to counter these detectors various anti-forensic techniques have been proposed. This led to a technique that utilized second order statistics of images for CE detection. In this letter, we propose an effective anti-forensic approach that performs CE without significant distortion in both the first and second order statistics of the enhanced image. We formulate an optimization problem using a variant of the well known Total Variation (TV) norm image restoration formulation. Experiments show that the algorithm effectively overcomes the first and second order statistics based detectors without loss in quality of the enhanced image.

A Real-Time Intelligent Jamming Attack of Wireless Sensor Networks

In recent years, multimedia forensics and security in Internet cloud environment have attached increasing attention. As a quintessential application of the Internet of Things, a wireless sensor network (WSN) provides means to facilitate human life and industrial development, but it also brings forward a lot of security issues. The majority of existing researches in the WSN security focus on link layer, network layer and application layer, but few deals with physical layer in real applications. In this work, we explore physical layer jamming and present an intelligent jamming model to attack against the WSN. We establish three key technologies to implement a practical intelligent jamming system: (1) burst signal detection based on waveform matching, (2) accurate waveform reconstruction and (3) monitoring the network status. We evaluate the performance of system in an indoor WSN test platform. Empirical results demonstrate that our system can immediately interfere with the packets already on the air, jam specific nodes and achieve adaptive jamming effects through adjustment of jamming power, jamming distance and jamming data length. Consequently, our intelligent jamming scheme is a real threat to wireless multimedia network security, requiring countermeasures for prevention.

센서 패턴 잡음을 이용한 디지털 영상 획득 장치 판별

With the advance of IT technology, contents from digital multimedia devices and softwares are widely used and distributed. However, novice uses them for illegal purpose and hence there are needs for protecting contents and blocking illegal usage through multimedia forensics. In this paper, we present a forensic technique for identifying digital imaging source using sensor pattern noise. First, the way to acquire the sensor pattern noise which comes from the imperfection of photon detector against light is presented. Then, the way to identify the similarity of digital imaging sources is explained after estimating the sensor pattern noises from the reference images and the unknown image. For the performance analysis of the proposed technique, 10 devices including DSLR camera, compact camera, smartphone and camcorder are tested and quantitatively analyzed. Based on the results, the proposed technique can achieve the 99.6% identification accuracy.

多媒体取证

Multimedia forensics is an important research area of information security, as well as a key technology of digital evidence authentication. Nowadays multimedia materials can be doctored very easily by pervasive editing software. A series of issues will arise if those fake multimedia materials are published, such as copyright of digital media, protection of personal privacy, social security, and so on. Thus examining the authenticity and integrity of digital media is of great signi cance. In the paper the characteristics and principles of multimedia forensics technology are introduced. Four important research topics of multimedia forensics, media source identi cation, tampering detection, processing history analysis, anti-forensics, are reviewed and summarized with related fundamental science and technology research progress. Finally the potential future trends of multimedia forensics are discussed.

Active query process for digital video surveillance forensic applications

Multimedia forensics is a new emerging discipline regarding the analysis and exploitation of digital data as support for investigation to extract probative elements. Among them, visual data about people and people activities, extracted from videos in an efficient way, are becoming day by day more appealing for forensics, due to the availability of large video-surveillance footage. Thus, many research studies and prototypes investigate the analysis of soft biometrics data, such as people appearance and people trajectories. In this work, we propose new solutions for querying and retrieving visual data in an interactive and active fashion for soft biometrics in forensics. The innovative proposal joins the capability of transductive learning for semi-supervised search by similarity and a typical multimedia methodology based on user-guided relevance feedback to allow an active interaction with the visual data of people, appearance and trajectory in large surveillance areas. Approaches proposed are very general and can be exploited independently by the surveillance setting and the type of video analytic tools.

Detection of JPEG double compression and identification of smartphone image source and post-capture manipulation

Digital multimedia forensics is an emerging field that has important applications in law enforcement and protection of public safety and national security. In digital imaging, JPEG is the most popular lossy compression standard and JPEG images are ubiquitous. Today's digital techniques make it easy to tamper JPEG images without leaving any visible clues. Furthermore, most image tampering involves JPEG double compression, it heightens the need for accurate analysis of JPEG double compression in image forensics. In this paper, to improve the detection of JPEG double compression, we transplant the neighboring joint density features, which were designed for JPEG steganalysis, and merge the joint density features with marginal density features in DCT domain as the detector for learning classifiers. Experimental results indicate that the proposed method improves the detection performance. We also study the relationship among compression factor, image complexity, and detection accuracy, which has not been comprehensively analyzed before. The results show that a complete evaluation of the detection performance of different algorithms should necessarily include image complexity as well as the double compression quality factor. In addition to JPEG double compression, the identification of image capture source is an interesting topic in image forensics. Mobile handsets are widely used for spontaneous photo capture because they are typically carried by their users at all times. In the imaging device market, smartphone adoption is currently exploding and megapixel smartphones pose a threat to the traditional digital cameras. While smartphone images are widely disseminated, the manipulation of images is also easily performed with various photo editing tools. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. Following the success of our previous work in JPEG double compression detection, we conducted a study to identify smartphone source and post-capture manipulation by utilizing marginal density and neighboring joint density features together. Experimental results show that our method is highly promising for identifying both smartphone source and manipulations. Finally, our study also indicates that applying unsupervised clustering and supervised classification together leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of the intentional post-capture manipulation on smartphone images.

On the reliability of forensic schemes using resampling for image copy-move forgery

The goal of image forgery detection in multimedia security and forensics is to find a clue of image manipulation and prove the inauthenticity of digital images. The presence of the original and its duplicated regions in an image is regarded as a fingerprint for copy-move forgery. The problem investigated here concerns the situation when resampling process is employed in the pre-processing stage of detecting image copy-move forgery. In several detection techniques of copy-move forgery in digital images, resampling is utilised to lower the spatial resolution of an image, further improving the efficiency and the speed of image forensics. However, the reliability of detection methods has not been examined in detail. In this paper, we take a view of some recently-proposed forensic techniques using resampling and analyse the reliability of detecting copy-move forgery, by modelling copy-move forgery process and detection problem. Based on theoretical analysis and experimental validation it is concluded from this study that a correct detection of image copy-move tampering may be impeded by resampling operation under certain conditions and it is more difficult to reveal the forgery than previously thought.

Joint Forensics-Scheduling Strategy for Delay-Sensitive Multimedia Applications over Heterogeneous Networks

High quality multimedia forensics service is increasingly critical for delay-sensitive applications over heterogeneous networks. Up to now, it is a challenging problem, where the demand for less forensics overhead, higher authentication level and smaller transmission delay needs to be reconciled with the limited and often dynamic network resources. Traditional multimedia forensics mechanisms, however, either overlook the available network resource or neglect the interaction between multimedia forensics and network scheduling. This work presents a novel framework for delay-sensitive multimedia applications over resource-limited heterogeneous networks by jointly considering multimedia forensics, network adaptation, and deadline-driven scheduling. In particular, we develop a joint forensics-scheduling scheme, which allocates the available network resources based on the affordable forensics overhead and expected quality of service, adaptively adjusts the scalable media-aware forensics, and schedules the transmissions to meet the application's delay constraints. Through analysis and simulation, we demonstrate that the proposed scheme not only can provide a satisfying multimedia forensics service with nearly full utilization of the network resource, but also can achieve substantial performance improvements compared to other reference approaches.

A NOVEL SOURCE MPEG-2 VIDEO IDENTIFICATION ALGORITHM

With the availability of powerful multimedia editing software, all types of personalized image and video resources are available in networks. Multimedia forensics technology has become a new topic in the field of information security. In this paper, a new source video system identification algorithm is proposed based on the features in the video stream; it takes full advantage of the different characteristics in the rate control module and the motion prediction module, which are two open parts in the MPEG-2 video compression standard, and combines a support vector machine classifier to build an intelligent computing system for video source identification. The experiments show this proposed algorithm can effectively identify video streams that come from a number of video coding systems.

Survey on techniques of digital multimedia forensics

Digital multimedia forensics is an emerging research field of information security.The research on digital multimedia forensics is important to ensure the credibility of digital multimedia data.Using digital image forensics as an example,the authors reviewed the current digital multimedia forensics techniques from the five aspects,including tamper detection,source device identification,authenticity verification,device component forensics,and the reliability of digital multimedia forensics.The authors focused on introducing typical algorithms,and meanwhile,pointed out the main problems in the current research and suggested the urgent topics for the future research.

Estimate of PRNU Noise Based on Different Noise Models for Source Camera Identification

Identification of the source that has generated a digital content is considered one of the main open issues in multimedia forensics community. The extraction of photo-response non-uniformity (PRNU) noise has been so far indicated as a mean to identify sensor fingerprint. Such a fingerprint can be estimated from multiple images taken by the same camera by means of a de-noising filtering operation. In this paper, the authors propose a novel method for estimating the PRNU noise in source camera identification. In particular, a MMSE digital filter in the un-decimated wavelet domain, based on a signal-dependent noise model, is introduced and compared with others commonly adopted for this purpose. A theoretical framework and experimental results are provided and discussed.

Traitor-Within-Traitor Behavior Forensics: Strategy and Risk Minimization

Multimedia security systems have many users with different objectives and they influence each other's performance and decisions. Behavior forensics analyzes how users with conflicting interests interact with and respond to each other. Such investigation enables a thorough understanding of multimedia security systems and helps the digital rights enforcer offer stronger protection of multimedia. This paper analyzes the dynamics among attackers during multiuser collusion. The colluders share not only the profit from the redistribution of multimedia but also the risk of being detected by the content owner, and an important issue in collusion is fairness of the attack (i.e., whether all attackers share the same risk) (e.g., whether they have the same probability of being detected). While they might agree so, some selfish colluders may break their fair-play agreement in order to further lower their risk. This paper investigates the problem of within traitors in multimedia forensics, in an effort to formulate the dynamics among attackers and understand their behavior to minimize their own risk and protect their own interests. As the first work on the analysis of this colluder dynamics, this paper explores some possible strategies that a selfish colluder can use to minimize his or her probability of being caught. We show that processing his or her fingerprinted copy before multiuser collusion helps a selfish colluder further lower his or her risk, especially when the colluded copy has high resolution and good quality. This paper also investigates the optimal precollusion processing strategies for selfish colluders to minimize their risk under the quality constraints