This PDF file contains the front matter associated with SPIE Proceedings Volume 7541, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

YASS is a steganographic algorithm for digital images that hides messages robustly in a key-dependent transform domain so that the stego image can be subsequently compressed and distributed as JPEG. Given the fact that state-of-the-art blind steganalysis methods of 2007, when YASS was proposed, were unable to reliably detect YASS, in this paper we steganalyze YASS using several recently proposed general-purpose steganalysis feature sets. The focus is on blind attacks that do not capitalize on any weakness of a specific implementation of the embedding algorithm. We demonstrate experimentally that twelve different settings of YASS can be reliably detected even for small embedding rates and in small images. Since none of the steganalysis feature sets is in any way targeted to the embedding of YASS, future modifications of YASS will likely be detectable by them as well.

The square root law holds that acceptable embedding rate is sublinear in the cover size, specifically O(square root of n), in order to prevent detection as the warden's data and thus detector power increases. One way to transcend this law, at least in the i.i.d.case, is to restrict the cover to a chosen subset whose distribution is close to that of altered data. Embedding is then performed on this subset; this replaces the problem of finding a small enough subset to evade detection with the problem of finding a large enough subset that possesses a desired type distribution. We show that one can find such a subset of size asymptotically proportional to n rather than the square root of n. This works in the case of both replacement and tampering: Even if the distribution of tampered data depends on the distribution of cover data, one can find a fixed point in the probability simplex such that cover data of that distribution yields stego data of the same distribution. While the transmission of a subset is not allowed, this is no impediment: wet paper codes can be used, else in the worst case a maximal desirable subset can be computed from the cover by both sender and receiver without communication of side information.

Steganalysis is used to detect hidden content in innocuous images. Many successful steganalysis algorithms use a large number of features relative to the size of the training set and suffer from a "curse of dimensionality": large number of feature values relative to training data size. High dimensionality of the feature space can reduce classification accuracy, obscure important features for classification, and increase computational complexity. This paper presents a filter-type feature selection algorithm that selects reduced feature sets using the Mahalanobis distance measure, and develops classifiers from the sets. The experiment is applied to a well-known JPEG steganalyzer, and shows that using our approach, reduced-feature steganalyzers can be obtained that perform as well as the original steganalyzer. The steganalyzer is that of Pevn´y et al. (SPIE, 2007) that combines DCT-based feature values and calibrated Markov features. Five embedding algorithms are used. Our results demonstrate that as few as 10-60 features at various levels of embedding can be used to create a classifier that gives comparable results to the full suite of 274 features.

In this paper, we propose a practical approach to minimizing embedding impact in steganography based on syndrome coding and trellis-coded quantization and contrast its performance with bounds derived from appropriate rate-distortion bounds. We assume that each cover element can be assigned a positive scalar expressing the impact of making an embedding change at that element (single-letter distortion). The problem is to embed a given payload with minimal possible average embedding impact. This task, which can be viewed as a generalization of matrix embedding or writing on wet paper, has been approached using heuristic and suboptimal tools in the past. Here, we propose a fast and very versatile solution to this problem that can theoretically achieve performance arbitrarily close to the bound. It is based on syndrome coding using linear convolutional codes with the optimal binary quantizer implemented using the Viterbi algorithm run in the dual domain. The complexity and memory requirements of the embedding algorithm are linear w.r.t. the number of cover elements. For practitioners, we include detailed algorithms for finding good codes and their implementation. Finally, we report extensive experimental results for a large set of relative payloads and for different distortion profiles, including the wet paper channel.

While historically we may have been overly trusting of photographs, in recent years there has been a backlash of sorts and the authenticity of photographs is now routinely questioned. Because these judgments are often made by eye, we wondered how reliable the human visual system is in detecting discrepancies that might arise from photo tampering. We show that the visual system is remarkably inept at detecting simple geometric inconsistencies in shadows, reflections, and perspective distortions. We also describe computational methods that can be applied to detect the inconsistencies that seem to elude the human visual system.

The analysis of lateral chromatic aberration forms another ingredient for a well equipped toolbox of an image forensic investigator. Previous work proposed its application to forgery detection¹ and image source identification.² This paper takes a closer look on the current state-of-the-art method to analyse lateral chromatic aberration and presents a new approach to estimate lateral chromatic aberration in a runtime-efficient way. Employing a set of 11 different camera models including 43 devices, the characteristic of lateral chromatic aberration is investigated in a large-scale. The reported results point to general difficulties that have to be considered in real world investigations.

Sensor fingerprint is a unique noise-like pattern caused by slightly varying pixel dimensions and inhomogeneity of the silicon wafer from which the sensor is made. The fingerprint can be used to prove that an image came from a specific digital camera. The presence of a camera fingerprint in an image is usually established using a detector that evaluates cross-correlation between the fingerprint and image noise. The complexity of the detector is thus proportional to the number of pixels in the image. Although computing the detector statistic for a few megapixel image takes several seconds on a single-processor PC, the processing time becomes impractically large if a sizeable database of camera fingerprints needs to be searched through. In this paper, we present a fast searching algorithm that utilizes special "fingerprint digests" and sparse data structures to address several tasks that forensic analysts will find useful when deploying camera identification from fingerprints in practice. In particular, we develop fast algorithms for finding if a given fingerprint already resides in the database and for determining whether a given image was taken by a camera whose fingerprint is in the database.

Several promising techniques have been recently proposed to bind an image or video to its source acquisition device. These techniques have been intensively studied to address performance issues, but the computational efficiency aspect has not been given due consideration. Considering very large databases, in this paper, we focus on the efficiency of the sensor fingerprint based source device identification technique.1 We propose a novel scheme based on tree structured vector quantization that offers logarithmic improvements in the search complexity as compared to conventional approach. To demonstrate the effectiveness of the proposed approach several experiments are conducted. Our results show that with the proposed scheme major improvement in search time can be achieved.

In this paper we concentrate on robust image watermarking (i.e. capable of resisting common signal processing operations and intentional attacks to destroy the watermark) based on image features. Kutter et al.⁷ motivated that well chosen image features survive admissible image distortions and hence can benefit the watermarking process. These image features are used as location references for the region in which the watermark is embedded. To realize the latter, we make use of previous work¹⁶ where a ring-shaped region, centered around an image feature is determined for watermark embedding. We propose to choose a specific sequence of image features according to strict criteria so that the image features have large distance to other chosen image features so that the ring shaped embedding regions do not overlap. Nevertheless, such a setup remains prone to insertion, deletion and substitution errors. Therefore we applied a two-step coding scheme similar to the one employed by Coumou and Sharma⁴ for speech watermarking. Our contribution here lies in extending Coumou and Sharma's one dimensional scheme to the two dimensional setup that is associated with our watermarking technique. The two-step coding scheme concatenates an outer Reed-Solomon error-correction code with an inner, blind, synchronization mechanism.

Semi-fragile video watermarking is a technology for detecting manipulations. It provides robustness against content-preserving manipulations as well as sensitivity to content-changing manipulations. To achieve this, robust content-describing features are applied. We use the SIFT keypoint detection as feature for our semifragile video watermarking scheme introduced in this work. SIFT (Scale Invariant Feature Transformation) detects points invariant to image scale and rotation and can be used for object matching after changing the 3D viewpoint, addition of noise and modifications in illumination. With the detected feature points we generate an authentication message, which is embedded with a robust video watermark. In the verification process we introduce a temporal filtering approach to reduce the distortions caused by content-preserving manipulations. We present experimental results demonstrating the robustness and sensitivity of our scheme.

We investigate the use of reversible pre-embedding transformations to enhance reversible watermarking schemes for images. We are motivated by the observation that a (non-reversible) sorting transformation dramatically increases the quality of the embedding when combined with a reversible watermark based on a generalized integer transform. In one example we obtain a PSNR gain of 23 dB using the pre-sorting approach over the regular embedding method for the same payload size. This may provide opportunities for increasing the embedding capacity by trading off the quality for a larger payload size. We test several reversible sorting approaches but these do not provide us any gain in the watermarking capacity or quality.

In this paper, we consider a forensic multimodal authentication framework based on binary hypothesis testing in random projections domain. We formulate a generic authentication problem taking into account several possible counterfeiting strategies. The authentication performance analysis is accomplished in the scope of Neyman- Pearson framework as well as for an average probability of error for both direct and random projections domains. Worst-case attack/acquisition channel leading to the worst performance loss in terms of Bhattacharyya distance reduction is presented. The obtained theoretical findings are also confirmed by results of computer simulation.

We describe how to exploit the formation and storage of an embedded image thumbnail for image authentication. The creation of a thumbnail is modeled with a series of filtering operations, contrast adjustment, and compression. We automatically estimate these model parameters and show that these parameters differ significantly between camera manufacturers and photo-editing software. We also describe how this signature can be combined with encoding information from the underlying full resolution image to further refine the signature's distinctiveness.

Wide availability of cheap high-quality printing techniques make document forgery an easy task that can easily be done by most people using standard computer and printing hardware. To prevent the use of color laser printers or color copiers for counterfeiting e.g. money or other valuable documents, many of these machines print Counterfeit Protection System (CPS) codes on the page. These small yellow dots encode information about the specific printer and allow the questioned document examiner in cooperation with the manufacturers to track down the printer that was used to generate the document. However, the access to the methods to decode the tracking dots pattern is restricted. The exact decoding of a tracking pattern is often not necessary, as tracking the pattern down to the printer class may be enough. In this paper we present a method that detects what CPS pattern class was used in a given document. This can be used to specify the printer class that the document was printed on. Evaluation proved an accuracy of up to 91%.

Digital watermarking has become a widely used security technology in the domain of digital rights management and copyright protection as well as in other applications. In this work, we show recent results regarding a particular security attack: Embedding a new message in a previously watermarked cover using the same key as the original message. This re-embedding can be the consequence of the absence of truly asymmetric watermarking solutions, especially if the watermark is to be detected in public. In public detection scenarios, every detector needs the same key the embedder used to watermark the cover. With knowledge of the embedding algorithm, everybody who is able to detect the message can also maliciously embed a new message with the same key over the old one. This scenario is relevant in the case that an attacker intends to counterfeit a copyright notice, transaction ID or to change an embedded authentication code. This work presents experimental results on mechanisms for identifying such multiple embeddings in a spreadspectrum patchwork audio watermarking approach. We demonstrate that under certain circumstances such multiple embedding can be detected by watermarking-forensics.

This paper deals with the security of the robust zero-bit watermarking technique "Broken Arrows" (BA),¹ which was invented and tested for the international challenge BOWS-2.² The results of the first episode of the challenge showed that BA is very robust and we proposed last year an enhancement called "Averaging Wavelet Coefficients" (AWC),³ which further strengthens the robustness against the worst attack disclosed during this BOWS-2's first episode.⁴ However, in the second and third episodes of the challenge, during which the pirates could observe plenty of pictures watermarked with the same secret key, security flaws have been revealed and discussed.⁵ Here we propose counterattacks to these security flaws, investigating BA and its variant AWC. We propose two counterattack directions: to use the embedding technique AWC instead of BA, and to regulate the system parameters to lighten the watermarking embedding footprint. We also discuss these directions in the context of traitor tracing.⁶ Experimental results show that following these recommendations is sufficient to counter these attacks.

Current image re-sampling detectors can reliably detect re-sampling in JPEG images only up to a Quality Factor (QF) of 95 or higher. At lower QFs, periodic JPEG blocking artifacts interfere with periodic patterns of re-sampling. We add a controlled amount of noise to the image before the re-sampling detection step. Adding noise suppresses the JPEG artifacts while the periodic patterns due to re-sampling are partially retained. JPEG images of QF range 75-90 are considered. Gaussian/Uniform noise in the range of 28-24 dB is added to the image and the images thus formed are passed to the re-sampling detector. The detector outputs are averaged to get a final output from which re-sampling can be detected even at lower QFs. We consider two re-sampling detectors - one proposed by Poposcu and Farid [1], which works well on uncompressed and mildly compressed JPEG images and the other by Gallagher [2], which is robust on JPEG images but can detect only scaled images. For multiple re-sampling operations (rotation, scaling, etc) we show that the order of re-sampling matters. If the final operation is up-scaling, it can still be detected even at very low QFs.

Re-quantization commonly occurs when digital multimedia content is being tampered with. Detecting requantization is therefore an important element for assessing the authenticity of digital multimedia content. In this paper, we introduce three features based on the observation that re-quantization (i) induces periodic artifacts and (ii) introduces discontinuities in the signal histogram. After validating the discriminative potential of these features with synthetic signals, we propose a system to detect JPEG re-compression. Both linear (FLD) and non-linear (SVM) classifications are investigated. Experimental results clearly demonstrate the ability of the proposed features to detect JPEG re-compression, as well as their competitiveness compared to prior approaches to achieve the same goal.

MP3 is the most popular audio format nowadays in our daily life, for example music downloaded from the Internet and file saved in the digital recorder are often in MP3 format. However, low bitrate MP3s are often transcoded to high bitrate since high bitrate ones are of high commercial value. Also audio recording in digital recorder can be doctored easily by pervasive audio editing software. This paper presents two methods for the detection of double MP3 compression. The methods are essential for finding out fake-quality MP3 and audio forensics. The proposed methods use support vector machine classifiers with feature vectors formed by the distributions of the first digits of the quantized MDCT (modified discrete cosine transform) coefficients. Extensive experiments demonstrate the effectiveness of the proposed methods. To the best of our knowledge, this piece of work is the first one to detect double compression of audio signal.

This paper provides an information theoretical description of biometric systems at the system level. A number of basic models to characterize performance of biometric systems are presented. All models compare performance of an automatic biometric recognition system against performance of an ideal biometric system that knows correct decisions. The correct decision can be visualized as an input to a new decision system, and the decision by an automatic recognition system is the output of this decision system. The problem of performance evaluation for a biometric recognition system is formulated as (1) the problem of finding the maximum information that the output of the system has about the input, and (2) the problem of finding the maximum distortion that the output can experience with respect to the input of the system to guarantee a bounded average probability of recognition error. The first formulation brings us to evaluation of capacity of a binary asymmetric and M-ary channels. The second formulation falls under the scope of rate-distortion theory. We further describe the problem of physical signature authentication used to authenticate a biometric acquisition device and state the problem of secured biometric authentication as the problem of joint biometric and physical signature authentication. One novelty of this work is in restating the problem of secured biometric authentication as the problem of finding capacity and rate-distortion curve for a secured biometric authentication system. Another novelty is in application of transductive methods from statistical learning theory to estimate the conditional error probabilities of the system. This set of parameters is used to optimize the system performance.

Security of biometric templates stored in a system is important because a stolen template can compromise system security as well as user privacy. Therefore, a number of secure biometrics schemes have been proposed that facilitate matching of feature templates without the need for a stored biometric sample. However, most of these schemes suffer from poor matching performance owing to the difficulty of designing biometric features that remain robust over repeated biometric measurements. This paper describes a scheme to extract binary features from fingerprints using minutia points and fingerprint ridges. The features are amenable to direct matching based on binary Hamming distance, but are especially suitable for use in secure biometric cryptosystems that use standard error correcting codes. Given all binary features, a method for retaining only the most discriminable features is presented which improves the Genuine Accept Rate (GAR) from 82% to 90% at a False Accept Rate (FAR) of 0.1% on a well-known public database. Additionally, incorporating singular points such as a core or delta feature is shown to improve the matching tradeoff.

One of the critical steps in designing a secure biometric system is protecting the templates of the users that are stored either in a central database or on smart cards. If a biometric template is compromised, it leads to serious security and privacy threats because unlike passwords, it is not possible for a legitimate user to revoke his biometric identifiers and switch to another set of uncompromised identifiers. One methodology for biometric template protection is the template transformation approach, where the template, consisting of the features extracted from the biometric trait, is transformed using parameters derived from a user specific password or key. Only the transformed template is stored and matching is performed directly in the transformed domain. In this paper, we formally investigate the security strength of template transformation techniques and define six metrics that facilitate a holistic security evaluation. Furthermore, we analyze the security of two wellknown template transformation techniques, namely, Biohashing and cancelable fingerprint templates based on the proposed metrics. Our analysis indicates that both these schemes are vulnerable to intrusion and linkage attacks because it is relatively easy to obtain either a close approximation of the original template (Biohashing) or a pre-image of the transformed template (cancelable fingerprints). We argue that the security strength of template transformation techniques must consider also consider the computational complexity of obtaining a complete pre-image of the transformed template in addition to the complexity of recovering the original biometric template.

In 1999 Juels and Wattenberg introduced the fuzzy commitment scheme. Fuzzy commitment is a particular realization of a binary biometric secrecy system with a chosen secret key. Three cases of biometric sources are considered, i.e. memoryless and totally-symmetric biometric sources, memoryless and input-symmetric biometric sources, and memoryless biometric sources. It is shown that fuzzy commitment is only optimal for memoryless totally-symmetric biometric sources and only at the maximum secret-key rate. Moreover, it is demonstrated that for memoryless biometric sources, which are not inputsymmetric, the fuzzy commitment scheme leaks information on both the secret key and the biometric data. Finally, a number of coding techniques are investigated for the case of totally-symmetric memoryless biometric data statistics.

Biohashing algorithms map biometric features randomly onto binary strings with user-specific tokenized random numbers. In order to protect biometric data, these binary strings, the Biohashes, are not allowed to reveal much information about the original biometric features. In the paper we analyse two Biohashing algorithms using scalar randomization and random projection respectively. With scalar randomization, multiple bits can be extracted from a single element in a feature vector. The average information rate of Biohashes is about 0.72. However, Biohashes expose the statistic information about biometric feature, which can be used to estimate the original feature. Using random projection method, a feature vector in n dimensional space can be converted into binary strings with length of m (m ≤ n). Any feature vector can be converted into 2^m different Biohashes. The random projection can roughly preserve Hamming distance between Biohashes. Moreover, the direction information about the original vector can be retrieved with Biohashes and the corresponding random vectors used in the projection. Although Biohashing can efficiently randomize biometric features, combining more Biohashes of the same user can leak essential information about the original feature.

A robust fingerprint minutiae hash generation algorithm is proposed in this paper to extract a binary secure hash bit string from each fingerprint minutia and its vicinity. First, ordering of minutiae points and rotation and translation geometric alignment of each minutiae vicinity are achieved; second, the ordered and aligned points are diversified by offsetting their coordinates and angles in a random way; and finally, an ordered binary minutia hash bit string is extracted by quantizing the coordinates and angle values of the points in the diversified minutiae vicinity. The generated hashes from all minutiae vicinities in the original template form a protected template, which can be used to represent the original minutia template for identity verification. Experiments show desirable comparison performance (average Equal Error Rate 0.0233 using the first two samples of each finger in FVC2002DB2_A) by the proposed algorithm. The proposed biometric reference requires less template storage capacity compared to their unprotected counterparts. A security analysis is also given for the proposed algorithm.

In camera identification using sensor noise, the camera that took a given image can be determined with high certainty by establishing the presence of the camera's sensor fingerprint in the image. In this paper, we develop methods to reveal counter-forensic activities in which an attacker estimates the camera fingerprint from a set of images and pastes it onto an image from a different camera with the intent to introduce a false alarm and, in doing so, frame an innocent victim. We start by classifying different scenarios based on the sophistication of the attacker's activity and the means available to her and to the victim, who wishes to defend herself. The key observation is that at least some of the images that were used by the attacker to estimate the fake fingerprint will likely be available to the victim as well. We describe the socalled "triangle test" that helps the victim reveal attacker's malicious activity with high certainty under a wide range of conditions. This test is then extended to the case when none of the images that the attacker used to create the fake fingerprint are available to the victim but the victim has at least two forged images to analyze. We demonstrate the test's performance experimentally and investigate its limitations. The conclusion that can be made from this study is that planting a sensor fingerprint in an image without leaving a trace is significantly more difficult than previously thought.

Several methods exist for printer identification from a printed document. We have developed a system that performs printer identification using intrinsic signatures of the printers. Because an intrinsic signature is tied directly to the electromechanical properties of the printer, it is difficult to forge or remove. There are many instances where existance of the intrinsic signature in the printed document is undesireable. In this work we explore texture based attacks on intrinsic printer identification from text documents. An updated intrinsic printer identification system is presented that merges both texture and banding features. It is shown that this system is scable and robust against several types of attacks that one may use in an attempt to obscure the intrinsic signature.

We present a steganographic scheme based on the contourlet transform which uses the contrast sensitivity function (CSF) to control the force of insertion of the hidden information in a perceptually uniform color space. The CIELAB color space is used as it is well suited for steganographic applications because any change in the CIELAB color space has a corresponding effect on the human visual system as is very important for steganographic schemes to be undetectable by the human visual system (HVS). The perceptual decomposition of the contourlet transform gives it a natural advantage over other decompositions as it can be molded with respect to the human perception of different frequencies in an image. The evaluation of the imperceptibility of the steganographic scheme with respect to the color perception of the HVS is done using standard methods such as the structural similarity (SSIM) and CIEDE2000. The robustness of the inserted watermark is tested against JPEG compression.

Image encryption process is jointed with reversible data hiding in this paper, where the data to be hided are modulated by different secret keys selected for encryption. To extract the hided data from the cipher-text, the different tentative decrypted results are tested against typical random distribution in both spatial and frequency domain and the goodnessof- fit degrees are compared to extract one hided bit. The encryption based data hiding process is inherently reversible. Experiments demonstrate the proposed scheme's effectiveness on natural and textural images, both in gray-level and binary forms.

In the paper we present a watermarking scheme developed to meet the specific requirements of audio annotation watermarking robust against DA/AD conversion (watermark detection after playback by loudspeaker and recording with a microphone). Additionally the described approach tries to achieve a comparably low detection complexity, so it could be embedded in the near future in low-end devices (e.g. mobile phones or other portable devices). We assume in the field of annotation watermarking that there is no specific motivation for attackers to the developed scheme. The basic idea for the watermark generation and embedding scheme is to combine traditional frequency domain spread spectrum watermarking with psychoacoustic modeling to guarantee transparency and alphabet substitution to improve the robustness. The synchronization and extraction scheme is designed to be much less computational complex than the embedder. The performance of the scheme is evaluated in the aspects of transparency, robustness, complexity and capacity. The tests reveals that 44% out of 375 tested audio files pass the simulation test for robustness, while the most appropriate category shows even 100% robustness. Additionally the introduced prototype shows an averge transparency of -1.69 in SDG, while at the same time having a capacity satisfactory to the chosen application scenario.

Though the current state of the art of image forensics permits to acquire very interesting information about image history, all the instruments developed so far focus on the analysis of single images. It is the aim of this paper to propose a new approach that moves the forensics analysis further, by considering groups of images instead of single images. The idea is to discover dependencies among a group of images representing similar or equal contents in order to construct a graph describing image relationships. Given the pronounced effect that images posted on the Web have on opinions and bias in the networked age we live in, such an analysis could be extremely useful for understanding the role of pictures in the opinion forming process. We propose a theoretical framework for the analysis of image dependencies and describe a simple system putting the theoretical principles in practice. The performance of the proposed system are evaluated on a few practical examples involving both images created and processed in a controlled way, and images downloaded from the web.

Digital multimedia such as images and videos are prevalent on today's internet and cause significant social impact, which can be evidenced by the proliferation of social networking sites with user generated contents. Due to the ease of generating and modifying images and videos, it is critical to establish trustworthiness for online multimedia information. In this paper, we propose novel approaches to perform multimedia forensics using compact side information to reconstruct the processing history of a document. We refer to this as FASHION, standing for Forensic hASH for informatION assurance. Based on the Radon transform and scale space theory, the proposed forensic hash is compact and can effectively estimate the parameters of geometric transforms and detect local tampering that an image may have undergone. Forensic hash is designed to answer a broader range of questions regarding the processing history of multimedia data than the simple binary decision from traditional robust image hashing, and also offers more efficient and accurate forensic analysis than multimedia forensic techniques that do not use any side information.

Content-aware resizing methods have recently been developed, among which, seam-carving has achieved the most widespread use. Seam-carving's versatility enables deliberate object removal and benign image resizing, in which perceptually important content is preserved. Both types of modifications compromise the utility and validity of the modified images as evidence in legal and journalistic applications. It is therefore desirable that image forensic techniques detect the presence of seam-carving. In this paper we address detection of seam-carving for forensic purposes. As in other forensic applications, we pose the problem of seam-carving detection as the problem of classifying a test image in either of two classes: a) seam-carved or b) non-seam-carved. We adopt a pattern recognition approach in which a set of features is extracted from the test image and then a Support Vector Machine based classifier, trained over a set of images, is utilized to estimate which of the two classes the test image lies in. Based on our study of the seam-carving algorithm, we propose a set of intuitively motivated features for the detection of seam-carving. Our methodology for detection of seam-carving is then evaluated over a test database of images. We demonstrate that the proposed method provides the capability for detecting seam-carving with high accuracy. For images which have been reduced 30% by benign seam-carving, our method provides a classification accuracy of 91%.

In digital image forensics, it is generally accepted that intentional manipulations of the image content are most critical and hence numerous forensic methods focus on the detection of such 'malicious' post-processing. However, it is also beneficial to know as much as possible about the general processing history of an image, including content-preserving operations, since they can affect the reliability of forensic methods in various ways. In this paper, we present a simple yet effective technique to detect median filtering in digital images-a widely used denoising and smoothing operator. As a great variety of forensic methods relies on some kind of a linearity assumption, a detection of non-linear median filtering is of particular interest. The effectiveness of our method is backed with experimental evidence on a large image database.

This paper proposes an efficient method to determine the concrete configuration of the color filter array (CFA) from demosaiced images. This is useful to decrease the degrees of freedom when checking for the existence or consistency of CFA artifacts in typical digital camera images. We see applications in a wide range of multimedia security scenarios whenever inter-pixel correlation plays an important role. Our method is based on a CFA synthesis procedure that finds the most likely raw sensor output for a given full-color image. We present approximate solutions that require only one linear filtering operation per image. The effectiveness of our method is demonstrated by experimental results from a large database of images.

The popularity of video sharing platforms such as Youtube has prompted the need for the development of efficient techniques for multimedia identification. Content fingerprinting is a promising solution for this problem, whereby a short "fingerprint" that captures robust and unique characteristics of a signal is computed from each multimedia document. This fingerprint is then compared with a database to identify the multimedia. Several fingerprinting techniques have been proposed in the literature and have been evaluated using experiments. To complement these experimental evaluations and gain a deeper understanding, this paper proposes a framework for theoretical modeling and analysis of content fingerprinting schemes. Analysis of some key modules for fingerprint encoding and matching are also presented under this framework.

Error correction codes of suitable redundancy are used for ensuring perfect data recovery in noisy channels. For iterative decoding based methods, the decoder needs to be initialized with proper confidence values, called the log likelihood ratios (LLRs), for all the embedding locations. If these confidence values or LLRs are accurately initialized, the decoder converges at a lower redundancy factor, thus leading to a higher effective hiding rate. Here, we present an LLR allocation method based on the image statistics, the hiding parameters and the noisy channel characteristics. It is seen that this image-dependent LLR allocation scheme results in a higher data-rate, than using a constant LLR across all images. The data-hiding channel parameters are learned from the image histogram in the discrete cosine transform (DCT) domain using a linear regression framework. We also show how the effective data-rate can be increased by suitably increasing the erasure rate at the decoder.

A number of methods have been proposed over the last decade for embedding information within deoxyribonucleic acid (DNA). Since a DNA sequence is conceptually equivalent to a unidimensional digital signal, DNA data embedding (diversely called DNA watermarking or DNA steganography) can be seen either as a traditional communications problem or as an instance of communications with side information at the encoder, similar to data hiding. These two cases correspond to the use of noncoding or coding DNA hosts, which, respectively, denote DNA segments that cannot or can be translated into proteins. A limitation of existing DNA data embedding methods is that none of them have been designed according to optimal coding principles. It is not possible either to evaluate how close to optimality these methods are without determining the Shannon capacity of DNA data embedding. This is the main topic studied in this paper, where we consider that DNA sequences may be subject to substitution, insertion, and deletion mutations.

In this paper, we consider a low complexity identification system for highly distorted images. The performance of the proposed identification system is analyzed based on the average probability of error. An expected improvement of the performance is obtained combining random projection transform and concept of bit reliability. Simulations based on synthetic and real data confirm the efficiency of the proposed approach.