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As a popular method for image and video retargeting, seam carving has been used for image/video forgery manipulation. Although significant advances have been made in detecting seam-carving forgery, there are very few contributions in exposing the forgery from recompressed JPEG images, especially the doctored images that are recompressed at the same or a lower quality. The detection is generally challenging because the recompression after tampering compromises the existing forgery traces. Aiming to address this problem, we propose a hybrid large-feature mining-based approach that contains multiple types of large features. Ensemble learning is used to deal with the high-feature dimensionality. This paper shows that the proposed approach effectively distinguishes the seam-carved JPEG images from untouched JPEG images and improves the detection accuracy. In our proposed multiple types of features, directional derivative-based feature set and Gabor residual-based feature set generally perform the best. This paper also indicates that feature selection may play an important role to greatly reduce the feature number while maintaining a better or comparable detection accuracy.

As one of the most interesting areas in cyber forensics, multimedia forensics faces many challenges as users are generating a humongous amount of data with different operations. Forgery detection and steganography detection are two hotspots in multimedia forensics. To solve some highly challenging problems in multimedia forensics, especially in image forensics, we will introduce in this tutorial large feature mining-based approaches with ensemble learning in image forgery detection, including seam-carving forgery and inpainting forgery in JPEG images with the subsequent anti-forensics' operations. We will also introduce deep learning and apply the well-known deep learning models that were transferred and used for image forgery detection and image steganalysis, which considerably improve the detection accuracy.

Several gene- or set-based association tests have been proposed recently in the literature. Powerful statistical approaches are still highly desirable in this area. In this paper we propose a novel statistical association test, which uses information of the burden component and its complement from the genotypes. This new test statistic has a simple null distribution, which is a special and simplified variance-gamma distribution, and its p-value can be easily calculated. Through a comprehensive simulation study, we show that the new test can control type I error rate and has superior detecting power compared with some popular existing methods. We also apply the new approach to a real data set; the results demonstrate that this test is promising.

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. The work successfully addresses the challenge in detecting JPEG down recompression.It greatly improves the detection of seam carving forgery under recompression anti-forensics.It sheds light on solving highly challenging multimedia forensics problems by large feature mining.

The rapid growth of the Internet and online social network activities makes possible to fast share a huge volume of various digital multimedia contents such as images and videos. However, the easy accessibility of pornographic contents by general users, particularly teenagers, is problematic as well as the rare availability of the legitimate benchmark datasets for the pornographic image detection makes the research rather challenging. In this paper, we present a transfer learning approach, for which the existing general deep learning network is adopted for pornographic image detection problem. We consider five well-known deep learning models, which include VGG16, MobilNet, InceptionV3, Xception, and ResNet50, and each general deep learning network architecture is fine-turned to detect pornographic images. The experimental result with NPDI pornographic database demonstrates the effectiveness of the proposed transfer learning approach with promising detection accuracy.

In this paper, we discuss the digital forensic procedure and techniques for analyzing the local artifacts from four popular Instant Messaging applications in Android. As part of our findings, the user chat messages details and contacts were investigated for each application. By using two smartphones with different brands and the latest Android operating systems as experimental objects, we conducted digital investigations in a forensically sound manner. We summarize our findings regarding the different Instant Messaging chat modes and the corresponding encryption status of artifacts for each of the four applications. Our findings can be helpful to many mobile forensic investigations. Additionally, these findings may present values to Android system developers, Android mobile app developers, mobile security researchers as well as mobile users.

Gene- and pathway-based variant association tests are important tools in finding genetic variants that are associated with phenotypes of interest. Although some methods have been proposed in the literature, powerful and robust statistical tests are still desirable in this area. In this study, we propose a statistical test based on decomposing the genotype data into orthogonal parts from which powerful and robust independent p-value combination approaches can be utilized. Through a comprehensive simulation study, we compare the proposed test with some existing popular ones. Our simulation results show that the new test has great performance in terms of controlling type I error rate and statistical power. Real data applications are also conducted to illustrate the performance and usefulness of the proposed test.

Phishing has been a widespread issue for many years, claiming countless victims, some of which have not even realized that they fell prey. The sole purpose of phishing is to obtain sensitive information from its victims. There have yet to be a consensus on the best way to detect phishing. In this paper, we analyze web-based phishing detection by using Random Forest. Some important URL features are identified and our study shows that the detection performance with feature selection is improved.

Steganalysis and forgery detection in image forensics are generally investigated separately. We have designed a method targeting the detection of both steganography and seam-carved forgery in JPEG images. We analyze the neighboring joint density of the DCT coefficients and reveal the difference between the untouched image and the modified version. In realistic detection, the untouched image and the modified version may not be obtained at the same time, and different JPEG images may have different neighboring joint density features. By exploring the self-calibration under different shift recompressions, we propose calibrated neighboring joint density-based approaches with a simple feature set to distinguish steganograms and tampered images from untouched ones. Our study shows that this approach has multiple promising applications in image forensics. Compared to the state-of-the-art steganalysis detectors, our approach delivers better or comparable detection performances with a much smaller feature set while detecting several JPEG-based steganographic systems including DCT-embedding-based adaptive steganography and Yet Another Steganographic Scheme (YASS). Our approach is also effective in detecting seam-carved forgery in JPEG images. By integrating calibrated neighboring density with spatial domain rich models that were originally designed for steganalysis, the hybrid approach obtains the best detection accuracy to discriminate seam-carved forgery from an untouched image. Our study also offers a promising manner to explore steganalysis and forgery detection together.

While seam carving has been widely used in computer vision and multimedia processing, it is also used for tampering illusions. Although several methods have been proposed to detect seam carving-based forgery, to this date, the detection of the seam carving forgery under recompression attacks in JPEG images has not been explored. To fill this gap, we proposed a hybrid large scale feature mining-based detection method to distinguish the doctored JPEG images from the untouched JPEG images under recompression attacks. Over one hundred thousand features from the spatial domain and from the DCT transform domain are extracted. 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 exposing the seam-carving forgery under recompression attacks, especially from a lower quality level or on the same quality recompression.

Inpainting, originally designed in computer vision to reconstruct lost or deteriorated parts of images and videos, has been used for image tampering, including region filling and object removal to alter the truth. While several types of tampering including copy-move and seam carving forgery can now be successfully exposed in image forensics, there has been very little study to tackle inpainting forgery in JPEG images, the detection of which is extremely challenging due to the post-recompression attacks performed to cover or compromise original inpainting traces. To date, there is no effective way to detect inpainting image forgery under combined recompression attacks. To fill such a gap in image forensics and reveal inpainting forgery from the post-recompression attacks in JPEG images, we propose in this paper an approach that begins with large feature mining in discrete transform domain, ensemble learning is then applied to deal with the high feature dimensionality and to prevent the overfitting that generally happens to some regular classifiers under high feature dimensions. Our study shows the proposed approach effectively exposes inpainting forgery under post recompression attacks, especially, it noticeably improves the detection accuracy while the recompression quality is lower than the original JPEG image quality, and thus bridges a gap in image forgery detection.

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.

The prosperity of mobile devices have been rapidly and drastically reforming the use pattern and of user habits with computing devices. Android, the most popular mobile operating system, has a privilege-separated security system through a sophisticated permission control mechanism. Android Apps need to request permissions to access sensitive personal data and system resources, but empirical studies have found that various types of malicious software could obtain permissions and attack systems and applications by deceiving users and the security mechanism. In this paper, we propose a novel machine learning approach to detect malware by mining the patterns of Permissions and API Function Calls acquired and used by Android Apps. Based on static analysis of source code and resource files of Android Apps, binary and numerical features are extracted for qualitative and quantitative evaluation. Feature selection methods are applied to reduce the feature dimension and enhance the efficiency. Different machine learning methods, including Support Vector Machines, Random Forest and Neural Networks, are applied and compared in classification. The experimental results show that the proposed approach delivers accurate detection of Android malware. We deem that the proposed approach could help raise users' awareness of potential risks and mitigate malware threats for Android devices.

Imbalanced data classification is one of the challenging problems in data mining and machine learning research. The traditional classification algorithms are often biased towards the majority class when learning from imbalanced data. Much work have been proposed to address this problem, including data re-sampling, algorithm modification, and cost-sensitive learning. However, most of them focus on one of these techniques. This paper proposes to utilize both algorithm modification and cost-sensitive learning based on decision-theoretic rough set (DTRS) model. In particular, we use naive Bayes classifier as the base classifier and modify it for imbalanced learning. For cost-sensitive learning, we adopt the systematic method from DTRS to derive required thresholds that have the minimum decision cost. Our experimental results on three well-known text classification databases show that unified DTRS provides similar performance on balanced class distribution, outperforms naive Bayes classifier on imbalanced datasets, and is competitive with other imbalanced learning classifier.

The threat posed by hackers, spies, terrorists, and criminals, etc. using steganography for stealthy communications and other illegal purposes is a serious concern of cyber security. Several steganographic systems that have been developed and made readily available utilize JPEG images as carriers. Due to the popularity of JPEG images on the Internet, effective steganalysis techniques are called for to counter the threat of JPEG steganography. In this article, we propose a new approach based on feature mining on the discrete cosine transform (DCT) domain and machine learning for steganalysis of JPEG images. First, neighboring joint density features on both intra-block and inter-block are extracted from the DCT coefficient array and the absolute array, respectively; then a support vector machine (SVM) is applied to the features for detection. An evolving neural-fuzzy inference system is employed to predict the hiding amount in JPEG steganograms. We also adopt a feature selection method of support vector machine recursive feature elimination to reduce the number of features. Experimental results show that, in detecting several JPEG-based steganographic systems, our method prominently outperforms the well-known Markov-process based approach.

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.

Steganography secretly embeds additional information in digital products, the potential for covert dissemination of malicious software, mobile code, or information is great. To combat the threat posed by steganography, steganalysis aims at the exposure of the stealthy communication. In this paper, a new scheme is proposed for steganalysis of JPEG images, which, being the most common image format, is believed to be widely used for steganography purposes as there are many free or commercial tools for producing steganography using JPEG covers. First, a recently proposed Markov approach [27] is expanded to the inter-block of the discrete cosine transform (DCT) and to the discrete wavelet transform (DWT). The features on the joint distributions of the transform coefficients and the features on the polynomial fitting errors of the histogram of the DCT coefficients are also extracted. All features are called original ExPanded Features (EPF). Next, the EPF features are extracted from the calibrated version; these are called reference EPF features. The difference between the original and the reference EPF features is calculated, and then the original EPF features and the difference are merged to form the feature vector for classification. To handle the large number of developed features, the feature selection method of support vector machine recursive feature elimination (SVM-RFE) and a method of multi-class support vector machine recursive feature elimination (MSVM-RFE) are used to select features for binary classification and multi-class classification, respectively. Finally, support vector machines are applied to the selected features for detecting stego-images. Experimental results show that, in comparison to the Markov approach [27], this new scheme remarkably improves the detection performance on several JPEG-based steganographic systems, including JPHS, CryptoBola, F5, Steghide, and Model based steganography.

To improve a recently developed mel-cepstrum audio steganalysis method, we present in this paper a method based on Fourier spectrum statistics and mel-cepstrum coefficients, derived from the second-order derivative of the audio signal. Specifically, the statistics of the high-frequency spectrum and the mel-cepstrum coefficients of the second-order derivative are extracted for use in detecting audio steganography. We also design a wavelet-based spectrum and mel-cepstrum audio steganalysis. By applying support vector machines to these features, unadulterated carrier signals (without hidden data) and the steganograms (carrying covert data) are successfully discriminated. Experimental results show that proposed derivative-based and wavelet-based approaches remarkably improve the detection accuracy. Between the two new methods, the derivative-based approach generally delivers a better performance.

Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfields' performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptive-network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based FIS, named ANFIS-SUB. We compare the performance of resultant FIS and study the effect of parameters. A real-world injection profile data set from the Daqing Oilfield, China is used. FIS are generated and tested using training and testing data from that data set. The impact of data quality on the performance of FIS is also studied. Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems. ANFIS-GRID outperforms ANFIS-SUB due to its simplicity in parameter selection and its fitness in the target problem.

With the booming of smart phone and high-speed wireless networks in recent years, applications and data have been shifting from desktop to mobile devices at a vigorous pace. Although mobile computing provides great convenience in daily life, it becomes vulnerable to various types of emerging attacks. User authentication plays an indispensable role in protecting computer systems and applications, but the development of touch screen hardware and user habit change post requirements for new authentication methods for mobile and tablets devices. In this paper, we present a robust user authentication scheme using both static and dynamic features of touch gestures. We take advantage of the pressure sensitivity of multi-touch screens to obtain irreproducible biometric patterns. Discriminative features such as distance, angle, and pressure are extracted from the touch-point data, and used in statistical analysis for verification. We tested our scheme in a variety of experiments that involved multiple volunteers to perform various gestures. The analysis of experimental results and user feedback indicate the proposed scheme delivers comprehensive measurements and accurate pattern classification for touch gestures. Based on these results, we conclude that the proposed scheme overcomes the limitations of the existing user authentication methods, and shows great potential to provide robust protection against unauthorized access.

Heartbleed is a catastrophic bug in the OpenSSL which is a widely used implementation of the Transport Layer Security (TLS) protocol on the Internet. The Heartbleed bug which allows attackers to get sensitive information such as the secret keys, the user names, passwords and the actual content is a serious security threat to E-commerce. We proposed a Snort rule-based approach to detect the heartbleed bug. Experiment results show that the presented approach is suit for detecting the Heartbleed bug.

Advanced Audio Coding (AAC), a lossy audio compression scheme, standardized by ISO and IEC, which was designed to be the successor of the MP3 format, generally obtains better sound quality than MP3 at similar bit rates. AAC is supported on Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bitrate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate.

Image tampering, being readily facilitated and proliferated by today’s digital techniques, is increasingly causing problems regarding the authenticity of images. As the most popular multimedia data, JPEG images can be easily tampered without leaving any clues; therefore, JPEG-based forensics , including the detection of double compression, interpolation, rotation, etc., has become an active research topic in multimedia forensics. Nevertheless, the interesting issue of detecting image tampering and its related operations by using the same quantization matrix has not been fully investigated. Aiming to detect such forgery manipulations under the same quantization matrix, we propose a detection method by using shift-recompression -based reshuffle characteristic features. Learning classifiers are applied to evaluating the efficacy. Our experimental results indicate that the approach is indeed highly effective in detecting image tampering and relevant manipulations with the same quantization matrix.

Background Combining information from different studies is an important and useful practice in bioinformatics, including genome-wide association study, rare variant data analysis and other set-based analyses. Many statistical methods have been proposed to combine p-values from independent studies. However, it is known that there is no uniformly most powerful test under all conditions; therefore, finding a powerful test in specific situation is important and desirable. Results In this paper, we propose a new statistical approach to combining p-values based on gamma distribution, which uses the inverse of the p-value as the shape parameter in the gamma distribution. Conclusions Simulation study and real data application demonstrate that the proposed method has good performance under some situations.

In mining massive datasets, often two of the most important and immediate problems are sampling and feature selection. Proper sampling and feature selection contributes to reducing the size of the dataset while obtaining satisfactory results in model building. Theoretically, therefore, it is interesting to investigate whether a given dataset possesses a critical feature dimension, or the minimum number of features that is required for a given learning machine to achieve "satisfactory" performance. (Likewise, the critical sampling size problem concerns whether, for a given dataset, there is a minimum number of data points that must be included in any sample for a learning machine to achieve satisfactory performance.) Here the specific meaning of "satisfactory" performance is to be defined by the user. This paper addresses the complexity of both problems in one general theoretical setting and shows that they have the same complexity and are highly intractable. Next, an empirical method is applied in an attempt to find the approximate critical feature dimension of datasets. It is demonstrated that, under generally reasonable assumptions pertaining to feature ranking algorithms, the critical feature dimension are successfully discovered by the empirical method for a number of datasets of various sizes. The results are encouraging in achieving significant feature size reduction and point to a promising way in dealing with big data. The significance of the existence of crucial dimension in datasets is also explained.

With the booming of smartphone and high-speed wireless networks in recent years, audio streaming and sharing become convenient and inexpensive, so that digital media is gradually replacing physical media. This trend has also led to more attacks to digital audio and its application. MP3 double compression, achieved by decompressing and recompressing audio to a different compression ratio, is a typical manipulation of audio for malicious purposes. In this paper, we propose an approach for detecting both up-transcoded and down-transcoded MP3 audio files and revealing the real compression quality based on statistical patterns extracted from quantized MDCT coefficients and their derivatives. To minimize the false prediction caused by individual characteristics of diversified audio clips, we generated reference audio signals by recompressing and calibrating the audio, and measured the differences between signal-based and reference-based features. Support vector machines and dynamic evolving neural-fuzzy inference systems were applied for binary and multi-class classifications. The experimental results show that our approach effectively detects MP3 double compression and exposes the audio processing history for digital forensics.

Content-aware scaling is a method for image retargeting. It has been widely used in image manipulation including tampering. To improve the detection of the forgery in JPEG images, we propose to merge calibrated neighboring joint density and a rich models-based approach that was originally designed for steganalysis. A feature selection algorithm is utilized to reduce the feature dimensionality in the merged feature set. Experimental results show that the high-dimensional detector consisting of calibrated neighboring joint density and rich model features noticeably improves the detection accuracy; and the application of feature selection method to the high-dimensional detector can further improve the detection accuracy by using a much smaller and optimized feature set.

Critical dimension is the minimum number of features that is required to ensure the performance of a learning machine to be “high”. This critical dimension is usually unique to the learning machine and the ranking algorithm combination. Medical- and bio-informatics datasets are different from most other datasets in that there is an imbalance in most of these datasets and a high prediction accuracy often depends upon not just the overall accuracy but also the true positive and the false negative rates. To find a medically and bio-informatically accurate critical dimension and for better analysis of such datasets we develop two evaluation models, one using all features and the other using critical number of features. The performance measurements such as accuracy, specificity, sensitivity, area under the curve, F-score and kappa values are compared. This paper shows that at the critical dimension the evaluation model shows good results for all performance measurements measured on most datasets studied. The difference in performance measurements obtained using only critical number and using all features is significantly less, i.e., there is not much difference in sensitivity, specificity and other measurements calculated.

Content-aware image resizing, also known as image retargeting, seam carving, content-aware scaling, is originally proposed to automatically remove the paths of least importance, known as seams, to reduce image size or insert seams to extend it, in order to display images without distortion on various media especially on mobile devices, such as smartphones and PDAs. Content-aware scaling also allows removing entire objects from photographs without observed clues, and hence it has been used to tamper images. Due to the ubiquity of JPEG images on various mobile devices, it is increasingly necessary to authenticate these JPEG images for legitimate purposes. To detect the content-aware-based forgery in JPEG images, in this paper, we merge shift-recompression-based characteristic features in spatial domain and shift-recompression-based neighboring joint density in DCT domain together; an ensemble classifier is used to discriminate forged JPEG images from intact JPEG images. We also transfer other popular JPEG-based steganalysis methods to detecting the forgery. Experimental results show that steganalysis methods are effective in detecting context-aware-based JPEG forgery and our method is superior to other compared detection methods.

Feature reduction is a major problem in data mining. Though traditional methods such as feature ranking and subset selection have been widely used, there has been little consideration given to assuring satisfactory performance of a learning machine in relation to the minimum of features required or the “critical dimension”. This critical dimension is unique to a specific dataset, learning machine, and ranking algorithm combination. The empirical methods demonstrate that many datasets show the existence of critical dimension. The dependence of this critical dimension on the learning machines and ranking algorithms could provide newer insights in understanding datasets, machine learning classifiers and ranking algorithms. The preliminary results of analysis show that the critical dimension depends largely on the feature ranking algorithm and that learning machines are less significant in determining the critical dimension.

In today's world, more and more applications are developed for mobile devices and ever-increasing amount of communication happens over mobile devices and mobile networks. Though providing mobility and convenience, mobile devices generally do not offer intensive computations like their wired counterparts. In this paper we propose a set of methods that aims to protect video, specifically Motion JPEG (MJPEG) video streams, using selective data encryption. More precisely, our algorithm selects the most critical pixel information of MJPEG video as the input of encryption to keep itself lightweight while choosing and encrypting additional less-important video information when the environment allows. The proposed method was originated from the fact that JPEG images are encoded using prioritized pixel information. Experiment results show that the proposed algorithm performs well in terms of frame rate and CPU load at playback.

Performance evaluation plays an important role in the rule induction and classification process. Classic evaluation measures have been extensively studied in the past. In recent years, cost-sensitive classification has received much attention. In a typical classification task, all types of classification errors are treated equally. In many practical cases, not all errors are equal. Therefore, it is critical to build a cost-sensitive classifier to minimize the expected cost. This also brings us to another important issue, namely, cost-sensitive classifier evaluations. The main objective is to investigate different aspects of this problem. We review five existing cost-sensitive evaluation measures and compare their similarities and differences. We find that the cost-sensitive measures provide consistent evaluation results comparing to classic evaluation measures in most cases. However, when applying different cost values to the evaluation, the differences between the performances of each algorithm change. It is reasonable to conclude that the evaluation results could change dramatically when certain cost values applied. Moreover, by using cost curves to visualize the classification results, performance and performance differences of different classifiers can be easily seen.

As smartphones are being widely used in daily lives, the images captured by smartphones become ubiquitous and may be used for legal purposes. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. In this paper, we propose a method to determine the smartphone camera source of a particular image and operations that may have been performed on that image. We first take images using different smartphones and purposely manipulate the images, including different combinations of double JPEG compression, cropping, and rescaling. Then, we extract the marginal density in low frequency coordinates and neighboring joint density features on intra-block and inter-block as features. Finally, we employ a support vector machine to identify the smartphone source as well as to reveal the operations. Experimental results show that our method is very promising for identifying both smartphone source and manipulations. Our study also indicates that applying unsupervised clustering and supervised classification together (clustering first, followed by classification) leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of intentional manipulation.

Image tampering, being widely facilitated and proliferated by today's digital techniques, is increasingly causing problems concerning the authenticity of digital images. As one of the most favorable compressed media, JPEG image can be easily tampered without leaving any visible clues. JPEG-based forensics, including the detection of double compression, interpolation, rotation, etc, has been actively performed. However, the detection of misaligned cropping and recompression, with the same quantization matrix that was once used to encode original JPEG images, has not been effectively expressed or ignored to some extent. Aiming to detect such manipulations for forensics purpose, in this paper, we propose an approach based on block artifacts caused by the manipulation with JPEG compression. Specifically, we propose a shift-recompression based detection method to identify the inconsistency of the block artifacts in doctored JPEG images. The learning classifiers are applied for classification. Experimental results show that our approach is very promising to detect misaligned cropping and recompression with the same quantization matrix and greatly improves the existing methods. Our detection method is also very effective to detect relevant copy-paste and composite forgery in JPEG images.

The "curse of dimensionality" problem, which occurs in many applications involving data mining such as biomedical informatics, digital forensics, risk management, etc., makes it difficult to develop accurate learning machine classifiers when the dataset includes too many irrelevant or insignificant features. Therefore, finding the smallest set of features necessary to obtain the most accurate classifier is an issue of great theoretical and practical interest. In efforts toward developing formal methods for finding the "critical dimension", this paper presents an empirical study of the minimum number of features that are required for a learning machine to perform accurately. The dataset is first featured ranked; then, iteratively, the least important feature is removed and the performance is plotted against the number of features; the point at which the performance curve drops significantly and does not rise again gives the critical dimension, which is a unique number for each specific combination of learning machine and feature ranking method. It is shown in this paper that the critical dimension phenomenon indeed exists for several of the bioinformatics datasets studied.

Recently well-designed adaptive steganographic systems, including ±1 embedding in the DCT domain with optimized costs to achieve the minimal-distortion [8], have posed serious challenges to steganalyzers. Additionally, although the steganalysis of Yet Another Steganographic Scheme (YASS) was actively conducted, the detection of the YASS steganograms by a large B-block parameter has not been well explored. In this paper, we aim to detect the state-of-the-art adaptive steganographic system in DCT-embedding and to improve the steganalysis of YASS. To detect DCT-embedding based adaptive steganography, we design the features of differential neighboring joint density on the absolute array of DCT coefficients between the original JPEG images and the calibrated versions. To discriminate YASS steganograms from covers, the candidate blocks that are possibly used for embedding and the non-candidate block neighbors that are impossibly used for information hiding are identified first. The difference of the neighboring joint density between candidate blocks and the non-candidate neighbors is obtained. Support Vector Machine (SVM) and logistic regression classifiers are employed for classification. Experimental results show that our approach is very promising when detecting DCT-embedding based adaptive steganography. Compared to the steganalysis based on CC-PEV feature set, our method greatly improves the detection accuracy; the advantage is especially noticeable in the detection of the steganograms with low relative payload. In steganalysis of YASS, our approach is superior to a previous well-known steganalysis algorithm; our method remarkably improves the detection accuracy especially in the detection of the YASS steganograms that are produced with a large B-block size, which was not well addressed before.

The objective of steganography is to implement covert communication by hiding data in digital files such that there is no indication of the existence of hidden data; so an ideal steganographic system should be able to withstand all steganalysis methods--or be undetectable. In image steganography, several information-hiding systems have been proposed for JPEG images. However, with the advances in steganalysis, it seems that fewer and fewer steganography systems are truly undetectable. In this paper, we design a new steganography called statistically invisible steganography (SIS) to hide data in JPEG images, which are invulnerable to current methods of steganalysis. Specifically, we embed data in the selective coefficients of the selective DCT blocks that have high complexity and maintain original statistics. Experimental results show that current techniques of steganalysis are unable to successfully detect SIS steganograms.

Steganography, the ancient art for secretive communications, has revived on the Internet by way of hiding secret data, in completely imperceptible manners, into a digital file. Thus, the steganography has created a serious threat to cyber security due to the covert channel it provides that can be readily exploited for various illegal purposes. Likewise, image tampering or forgery, which has been greatly facilitated and proliferated by photo processing tools, is increasingly causing problems concerning the authenticity of digital images. JPEG images constitute one of the most popular media on the Internet; yet they can be easily used for the steganography as well as easily tampered by, e.g., removing, adding, or splicing objects without leaving any clues. Therefore, there is a critical need to develop reliable methods for steganalysis (analysis of multimedia for the steganography) and for forgery detection in JPEG images to serve applications in national security, law enforcement, cybercrime fighting, digital forensics, and network security, etc. This article presents some recent results on detecting JPEG steganograms, doubly compressed JPEG images, and resized JPEG images based on a unified framework of feature mining and pattern recognition approaches. At first, the neighboring joint density features and marginal density features of the DCT coefficients of the JPEG image are extracted; then learning classifiers are applied to the features for the detection. Experimental results indicate that the method prominently improves the detection performances in JPEG images when compared to a previously well-studied method. Also, it is demonstrated that detection performance deteriorates with increasing image complexity; hence, a complete evaluation of the detection performance of different algorithms should include image complexity—in addition to other relevant factors such as hiding ratio or compression ratio—as a significant and independent parameter.

Digital multimedia forensics is an emerging field that has important applications in law enforcement, the protection of public safety, and notational security. As a popular image compression standard, the JPEG format is widely adopted; however, the tampering of JPEG images can be easily performed without leaving visible clues, and it is increasingly necessary to develop reliable methods to detect forgery in JPEG images. JPEG double compression is frequently used during image forgery, and it leaves a clue to the manipulation. To detect JPEG double compression, we propose in this paper to extract the neighboring joint density features and marginal density features on the DCT coefficients, and then to apply learning classifiers to the features for detection. Experimental results indicate that the proposed method delivers promising performance in uncovering JPEG-based double compression. In addition, we analyze the relationship among compression quality factor, image complexity, and the performance of our double compression detection algorithm, and demonstrate that a complete evaluation of the detection performance of different algorithms should necessarily include both the image complexity and double compression quality factor.

MP3 is the most popular format for audio storage and a de facto standard of digital audio compression for the transfer and playback. The flexibility of compression ratio of MP3 coding enables users to choose their customized configuration in the trade-off between file size and quality. Double MP3 compression often occurs in audio forgery, steganography and quality faking by transcoding an MP3 audio to a different compression ratio. To detect double MP3 compression, in this paper, we extract the statistical features on the modified discrete cosine transform, and apply support vector machines and a dynamic evolving neuron-fuzzy inference system to the extracted features for classification. Experimental results show that our method effectively and accurately detects double MP3 compression for both up-transcoded and down-transcoded MP3 files. Our study also indicates the potential for mining the audio processing history for forensic purposes.

Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA), which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: Support Vector Machine Recursive Feature Elimination (SVMRFE), Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS), Gradient based Leave-one-out Gene Selection (GLGS). To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II) breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC) is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and AUC errors.

Today's ubiquitous digital media are easily tampered by, e.g., removing or adding objects from or into images without leaving any obvious clues. JPEG is a most widely used standard in digital images and it can be easily doctored. It is therefore necessary to have reliable methods to detect forgery in JPEG images for applications in law enforcement, forensics, etc. In this paper, based on the correlation of neighboring Discrete Cosine Transform (DCT) coefficients, we propose a method to detect resized JPEG images and spliced images, which are widely used in image forgery. In detail, the neighboring joint density features of the DCT coefficients are extracted; then Support Vector Machines (SVM) are applied to the features for detection. To improve the evaluation of JPEG resized detection, we utilize the shape parameter of generalized Gaussian distribution (GGD) of DCT coefficients to measure the image complexity.The study shows that our method is highly effective in detecting JPEG images resizing and splicing forgery. In the detection of resized JPEG image, the performance is related to both image complexity and resize scale factor. At the same scale factor, the detection performance in high image complexity is, as can be expected, lower than that in low image complexity.

Detection of information-hiding in JPEG images is actively delivered in steganalysis community due to the fact that JPEG is a widely used compression standard and several steganographic systems have been designed for covert communication in JPEG images. In this paper, we propose a novel method of JPEG steganalysis. Based on an observation of bi-variate generalized Gaussian distribution in Discrete Cosine Transform (DCT) domain, neighboring joint density features on both intra-block and inter-block are extracted. Support Vector Machines (SVMs) are applied for detection. Experimental results indicate that this new method prominently improves a current art of steganalysis in detecting several steganographic systems in JPEG images. Our study also shows that it is more accurate to evaluate the detection performance in terms of both image complexity and information hiding ratio.

We apply manifold learning to a real data set of distressed and healthy companies for proper geometric tunning of similarity data points and visualization. While Isomap algorithm is often used in unsupervised learning our approach combines this algorithm with information of class labels for bankruptcy prediction. We compare prediction results with classifiers such as Support Vector Machines (SVM), Relevance Vector Machines (RVM) and the simple k-Nearest Neighbor (KNN) in the same data set and we show comparable accuracy of the proposed approach.

In this article, based on the Markov approach proposed by shi et al., we expand it to the inter-blocks of the DCT domain, calculate the difference of the expanded Markov features between the testing image and the calibrated version, and combine these difference features and the polynomial fitting features on the histogram of the DCT coefficients as detectors. We reasonably improve the detection performance in multi-class JPEG images. We also compare the steganalysis performance among the feature reduction/selection methods based on principal component analysis, singular value decomposition, and Fisherpsilas linear discriminant.

One of the Internetpsilas hallmark is the rapid spread of the use of information and communication technology. This has boosted methods for hiding stego information inside digital cover content images which is a concerning issue in information security. On the other hand, attack of steganographic schemes has leveraged methods for steganalysis which is a challenging problem. In this paper, first we look at the design of classifiers, such as, support vector machines (SVM) and neural networks (RBF and MLP) which are able to detect the presence of least significant bit (LSB) matching steganography of gray scale images. Second, by combining with feature ranking methods (SVM-recursive feature elimination, Kruskal Wallis) and reduction techniques (PCA) pattern classification of stego is successfully achieved. It is of utmost importance to look at the large set of features extracted from images and find ranking methods able, namely, to exclude correlated and redundant features, avoid the curse of dimensionality or circumvent the need of the steganalyzer to be re-designed. Results show that desirable properties of robustness and resilience are attained by designing classifiers able to deal with redundancy and noise. Moreover, comparison of classifiers performance emphasizes the chosen model for the steganalyser.

Background Comprehensive evaluation of common genetic variations through association of single nucleotide polymorphisms (SNPs) with complex human diseases on the genome-wide scale is an active area in human genome research. One of the fundamental questions in a SNP-disease association study is to find an optimal subset of SNPs with predicting power for disease status. To find that subset while reducing study burden in terms of time and costs, one can potentially reconcile information redundancy from associations between SNP markers. Results We have developed a feature selection method named Supervised Recursive Feature Addition (SRFA). This method combines supervised learning and statistical measures for the chosen candidate features/SNPs to reconcile the redundancy information and, in doing so, improve the classification performance in association studies. Additionally, we have proposed a Support Vector based Recursive Feature Addition (SVRFA) scheme in SNP-disease association analysis. Conclusions We have proposed using SRFA with different statistical learning classifiers and SVRFA for both SNP selection and disease classification and then applying them to two complex disease data sets. In general, our approaches outperform the well-known feature selection method of Support Vector Machine Recursive Feature Elimination and logic regression-based SNP selection for disease classification in genetic association studies. Our study further indicates that both genetic and environmental variables should be taken into account when doing disease predictions and classifications for the most complex human diseases that have gene-environment interactions.

In this article, we propose a scheme of detecting the information-hiding in videos based on the pairs of condition and joint distributions in the transform domains. Specifically, based on the approach of the Markov-process in JPEG image steganalysis and our previous work, we propose the pairs of condition and joint distribution of the neighbor difference in the transform domains, including discrete cosine transform (DCT) and the discrete wavelet transform (DWT). We apply learning classifiers to the pairs extracted from the video covers and the video steganograms produced by MSU Video Steganograms. Experimental results show that this approach is very successful in detecting the information-hiding in MSU stego video steganograms.