Attributing synthetic images to their source generative models is critical for digital forensics and security. While most existing attribution methods can distinguish images produced by known models and reject those from unknown ones, they are unable to verify whether a given image was produced by a specific, previously unseen model. To address this limitation, we formulate an open-set verification problem: determining whether a given image was generated by a specific model. Our key insight is that synthetic images from different models show consistent, content-independent fingerprints in their amplitude spectrum. Based on this insight, we design a dynamic fingerprint simulator capable of simulating over 1.6 trillion generative model architectures. We further train an extractor to capture model-specific fingerprint representations with supervised contrastive learning, enabling accurate attribution of synthetic images, even from previously unseen models. Our method does not rely on any synthetic images, instead, it is trained solely on real images. On DMDetection and AIGCBenchmark, which comprises dozens of state-of-the-art and in-the-wild generative models, our method improves the attribution performance (AUC) of the prior method from random level to 94.05% and 83.05%, respectively. On GenImage and OSMA datasets, we obtain 85.08%, and 88.48% OSCR, outperforming the SOTA methods by 4.30% and 9.37% under the same settings.

Abstract Partially manipulated images pose a growing threat to the reliability of online content. The rapid spread of diffusion-based inpainting tools has made the creation of such manipulations increasingly easy to perform. As a result, the multimedia forensics community is disadvantaged compared to the attackers, as developing effective localization techniques often requires the creation of large datasets, a resource-intensive process due to the necessary human effort. In this paper, we present Beyond the Brush+ + (BtB++), a fully automated pipeline for generating large-scale datasets of realistic inpainted images. Our experiments demonstrate that BtB++ is both flexible and easily integrates different models and configurations, offering the adaptability required to address evolving models and application scenarios. Moreover, an automatic filtering mechanism ensures quality control by discarding low-quality generated images. To provide an initial assessment of the proposed filtering strategy, we also conducted a small-scale human evaluation, studying the alignment between human perceptual judgments and the automatic metrics used for filtering.

Determining the origin of a digital image or video, namely device source identification, is widely used in courtroom evidence and copyright protection. Currently, device source identification primarily focuses on images captured using single camera with default settings. However, with the advancement of imaging technology, there is a large number of smartphones equipped with multiple cameras and various shooting modes for acquiring images, which may pose a significant challenge to device source identification. Therefore, to assess the performance of image source identification algorithm for modern smartphones and promote further research, it is crucial to build a dataset of image and video captured by modern smartphones. In this paper, we present a large-scale image and video dataset for forensic analysis, ForensiCam-215K. The dataset includes over 215K media contents captured by 130 modern smartphones of 10 major brands. We used the latest equipment to capture images from the main, wide-angle, and telephoto cameras in six different shooting modes, and the media were collected under a strictly controlled procedure to reduce the bias caused by differences in the acquisition process between different devices. Additionally, we used the Photo Response Non-Uniformity (PRNU) method to perform device source identification tests on the dataset. The results indicate that device source identification is a challenging task especially for images and videos captured by smartphones with multiple cameras and various shooting modes. The dataset will be released as open-source and freely available for use by the multimedia forensics research community at https://github.com/dswdsw21072/ForensiCam-215K.

The increasing prevalence of deepfake audio technologies and their potential for malicious use in fields such as politics and media has raised significant concerns regarding the ability to distinguish fake from authentic audio recordings. This study proposes a robust technique for detecting synthetic audio by leveraging three spectral features: Linear Frequency Cepstral Coefficients (LFCC), Mel Frequency Cepstral Coefficients (MFCC), and Constant Q Cepstral Coefficients (CQCC). These features are processed using an enhanced ResNeXt architecture to improve classification accuracy between genuine and spoofed audio. Additionally, a Multi-Layer Perceptron (MLP)-based fusion technique is employed to further boost the model’s performance. Extensive experiments were conducted using three datasets: the ASVspoof 2019 Logical Access (LA) dataset—featuring text-to-speech (TTS) and voice conversion attacks—the ASVspoof 2019 Physical Access (PA) dataset—including replay attacks—and the ASVspoof 2021 LA, PA and DF datasets. The proposed approach has demonstrated superior performance compared to state-of-the-art methods across all three datasets, particularly in detecting fake audio generated by text-to-speech (TTS) attacks. Its overall performance is summarized as follows: the system achieved an Equal Error Rate (EER) of 1.05% and a minimum tandem Detection Cost Function (min-tDCF) of 0.028 on the ASVspoof 2019 Logical Access (LA) dataset, and an EER of 1.14% and min-tDCF of 0.03 on the ASVspoof 2019 Physical Access(PA) dataset, demonstrating its robustness in detecting various types of audio spoofing attacks. Finally, on the ASVspoof 2021 LA dataset the method achieved an EER of 7.44% and min-tDCF of 0.35.

Speckle noise produces a strong degradation in SAR images, characterized by a multiplicative model. Its removal is an important step of any processing chain exploiting such data. To perform this task, several model-based despeckling methods were proposed in the past years as well as, more recently, deep learning approaches. However, most of the latter ones need to be trained on a large number of pairs of noisy and clean images that, in the case of SAR images, can only be produced with the aid of synthetic noise. In this paper, we propose a self-supervised learning method based on the use of Deep Image Prior, which is extended to deal with speckle noise. The major advantage of the proposed approach lies in its ability to perform denoising without requiring any reference clean image during training. A new loss function is introduced in order to reproduce a multiplicative noise having statistics close to those of a typical speckle noise and composed also by a guidance term derived from model-based denoisers. Experimental results are presented to show the effectiveness of the proposed method and compare its performance with other reference despeckling algorithms. • A self-supervised learning method for SAR despeckling, denoted as S3DIP, is proposed. • The Deep Image Prior (DIP) approach is extended introducing a learnable noise matrix. • A histogram loss based on the knowledge of the speckle noise statistics is designed. • Existing model-based denoisers are exploited through the use of a guidance loss. • S3DIP outperforms both DIP and existing model-based denoisers on the intended task.

Over the past decade, the proliferation of social networks introduced new challenges in the multimedia forensic field, such as the identification of the originating platform. Significant strides have been made in the characterization of digital images, exploiting features related to the media container and content. Within the realm of videos, several efforts have been directed towards analyzing the container aspect. However, the utilization of content-based features remains limited due to the intricate nature of video encoding. In this paper, we introduce an approach to identify the source social network of a digital video by leveraging codec-based features. For the purpose, we designed a method to extract and efficiently organize detailed information from H.264/AVC-encoded videos based on a bespoke version of the video decoder tool JM. We show how the proposed method can significantly improve the process of determining the source social network, even when confronted with container-based laundering operations, surpassing existing state-of-the-art results.

Detecting the origin of a digital video within a social network is a critical task that aids law enforcement and intelligence agencies in identifying the creators of misleading visual content. In this research, we introduce an innovative method for identifying the original social network of a video, even when the video has been altered through actions like group of frames removal and file container reconstruction. The proposed method takes advantage of the video encoding’s temporal uniformity, leveraging motion vectors to characterize the specific features associated to various social media platforms. Each video is represented by a graph where nodes correspond to macroblocks. These macroblocks are interconnected by following the inter-prediction rules outlined in the H.264/AVC codec standard. Such a structure can be then classified using a graph neural network to predict the platform on which the video has been shared. Experimental results demonstrate that this approach outperforms both codec- and content-based approaches, underscoring the effectiveness of a structural approach in attributing the social media platform from which videos originated.

Abstract The forensic analysis of digital videos is becoming increasingly relevant to deal with forensic cases, propaganda, and fake news. The research community has developed numerous forensic tools to address various challenges, such as integrity verification, manipulation detection, and source characterization. Each tool exploits characteristic traces to reconstruct the video life-cycle. Among these traces, a significant source of information is provided by the specific way in which the video has been encoded. While several tools are available to analyze codec-related information for images, a similar approach has been overlooked for videos, since video codecs are extremely complex and involve the analysis of a huge amount of data. In this paper, we present a new tool designed for extracting and parsing a plethora of video compression information from H.264 encoded files, including macroblocks structure, prediction residuals, and motion vectors. We demonstrate how the extracted features can be effectively exploited to address various forensic tasks, such as social network identification, source characterization, and double compression detection. We provide a detailed description of the developed software, which is released free of charge to enable its use by the research community to create new tools for forensic analysis of video files.

The extensive spread of fake news on social networks is carried out by a diverse range of users, encompassing private individuals, newspapers, and organizations. With widely accessible image and video editing tools, malicious users can easily create manipulated media. They can then distribute this content through multiple fake profiles, aiming to maximize its social impact. To tackle this problem effectively, it is crucial to possess the ability to analyze shared media to identify the originators of fake news. To this end, multimedia forensics research has advanced tools that examine traces in media, revealing valuable insights into its origins. While combining these tools has proven to be highly efficient in creating profiles of image and video creators, it is important to note that most of these tools are not specifically designed to function effectively in the complex environment of content exchange on social networks. In this paper, we introduce the problem of establishing associations between images and their source profiles as a means to tackle the spread of disinformation on social platforms. To this end, we assembled SocialNews, an extensive image dataset comprising more than 12,000 images sourced from 21 user profiles across Facebook, Instagram, and Twitter, and we propose three increasingly realistic and challenging experimental scenarios. We present two simple yet effective techniques as benchmarks, one based on statistical analysis of Discrete Cosine Transform (DCT) coefficients and one employing a neural network model based on ResNet, and we compare their performance against the state of the art. Experimental results show that the proposed approaches exhibit superior performance in accurately classifying the originating user profiles.

The popularity of social networks as primary mediums for sharing visual content has made it crucial for forensic experts to identify the original platform of multimedia content. Various methods address this challenge, but the constant emergence of new platforms and updates to existing ones often render forensic tools ineffective shortly after release. This necessitates the regular updating of methods and models, which can be particularly cumbersome for techniques based on neural networks which cannot quickly adapt to new classes without sacrificing performance on previously learned ones – a phenomenon known as catastrophic forgetting. Recently, researchers aimed at mitigating this problem via a family of techniques known as continual learning. In this paper we study the applicability of continual learning techniques to the social network identification task by evaluating two relevant forensic scenarios: Incremental Social Platform Classification, for handling newly introduced social media platforms, and Incremental Social Version Classification, for addressing updated versions of a set of existing social networks. We perform an extensive experimental evaluation of a variety of continual learning approaches applied to these two scenarios. Experimental results demonstrate that, although Continual Social Network Identification remains a difficult problem, catastrophic forgetting can be significantly mitigated in both scenarios by retaining only a fraction of the image patches from past task training samples or by employing previous tasks prototypes.

The amount of multimedia content shared everyday, combined with the level of realism reached by recent fake-generating technologies, threatens to impair the trustworthiness of online information sources. The process of uploading and sharing data tends to hinder standard media forensic analyses, since multiple re-sharing steps progressively hide the traces of past manipulations. At the same time though, new traces are introduced by the platforms themselves, enabling the reconstruction of the sharing history of digital objects, with possible applications in information flow monitoring and source identification. In this work, we propose a supervised framework for the reconstruction of image sharing chains on social media platforms. The system is structured as a cascade of backtracking blocks, each of them tracing back one step of the sharing chain at a time. Blocks are designed as ensembles of classifiers trained to analyse the input image independently from one another by leveraging different feature representations that describe both content and container of the media object. Individual decisions are then properly combined by a late fusion strategy. Results highlight the advantages of employing multiple clues, which allow accurately tracing back up to three steps along the sharing chain.

Identifying the originating social network of a digital video is considered a relevant task to support law enforcement agencies and intelligence services in tracing producers of deceptive visual contents. Recent advances in video forensics highlighted how the structure of video containers can be extremely effective in determining the social network of provenance. However, current studies do not consider that a malicious user could easily launder the traces of the social network by rebuilding the container without transcoding. In this letter, we propose a method to identify a video’s originating social network, even when the video container structure is completely unreliable. The proposed method exploits the statistics of DCT coefficients to characterize the different social media encoding properties. With this work, we also built and made available over 1000 videos of different provenance (native, manipulated, exchanged through social networks) to aid the forensic community further researching this topic.

The image source identification task is mainly addressed by exploiting the unique traces of the sensor pattern noise, that ensure a negligible false alarm rate when comparing patterns extracted from different devices, even of the same brand or model. However, most recent smartphones are equipped with proprietary in-camera processing that can possibly expose unexpected correlated patterns within images belonging to different sensors.In this paper, we first highlight that wrong source attribution can happen on smartphones belonging to the same brand when images are acquired both in default and in bokeh mode. While the bokeh mode is proved to introduce a correlated pattern due to the specific in-camera post-processing, we also show that natural images also expose such issue, even when a reference from flat images is available. Furthermore, different camera models expose different correlation patterns since they are reasonably related to developers’ choices. Then, we propose a general strategy that allows the forensic practitioner to determine whether a questioned device may suffer from these correlated patterns, thus avoiding the risk of false image attribution.

To decide whether a digital video has been captured by a given device, multimedia forensic tools usually exploit characteristic noise traces left by the camera sensor on the acquired frames. This analysis requires that the noise pattern characterizing the camera and the noise pattern extracted from video frames under analysis are geometrically aligned. However, in many practical scenarios this does not occur, thus a re-alignment or synchronization has to be performed. Current solutions often require time consuming search of the realignment transformation parameters. In this paper, we propose to overcome this limitation by searching scaling and rotation parameters in the frequency domain. The proposed algorithm tested on real videos from a well-known state-of-the-art dataset shows promising results.

The Variation of Prediction Footprint (VPF), formerly used in video forensics for double compression detection and GOP size estimation, is comprehensively investigated to improve its acquisition capabilities and extend its use to video sequences that contain bi-directional frames (B-frames). By relying on a universal rate-distortion analysis applied to a generic double compression scheme, we first explain the rationale behind the presence of the VPF in double compressed videos and then justify the need of exploiting a new source of information such as the motion vectors, to enhance the VPF acquisition process. Finally, we describe the shifted VPF induced by the presence of B-frames and detail how to compensate the shift to avoid misguided GOP size estimations. The experimental results show that the proposed Generalized VPF (G-VPF) technique outperforms the state of the art, not only in terms of double compression detection and GOP size estimation, but also in reducing computational time.

The detection of images that are spliced from multiple sources is one important goal of image forensics. Several methods have been proposed for this task, but particularly since the rise of social media, it is an ongoing challenge to devise forensic approaches that are highly robust to common processing operations such as strong JPEG recompression and downsampling.In this work, we make a first step towards a novel type of cue for image splicing, which is based on the color formation of an image. We make the assumption that the color formation is a joint result of the camera hardware, the software settings, and the depicted scene, and as such can be used to locate spliced patches that originally stem from different images. To this end, we train a two-stage classifier on the full set of colors from a Macbeth color chart, and compare two patches for their color consistency. Our preliminary results on a challenging dataset on downsampled data of identical scenes indicate that the color distribution can be a useful forensic tool that is highly resistant to JPEG compression.

This paper proposes a novel approach for registering the PRNU pattern between different camera acquisition modes by relying on the imaged scene content. First, images are aligned by establishing correspondences between local descriptors: The result can then optionally be refined by maximizing the PRNU correlation. Comparative evaluations show that this approach outperforms those based on brute-force and particle swarm optimization in terms of reliability, accuracy and speed. The proposed scene-based approach for PRNU pattern alignment is suitable for video source identification in multimedia forensics applications.

Video forensics keeps developing new technologies to verify the authenticity and the integrity of digital videos. While most of the existing methods rely on the analysis of the video data stream, recently, a new line of research was introduced to investigate video life cycle based on the analysis of the video container. Anyway, existing contributions in this field are based on manual comparison of video container structure and content, which is time demanding and error-prone. In this paper, we introduce a method for unsupervised analysis of video file containers, and present two main forensic applications of such method: the first one deals with video integrity verification, based on the dissimilarity between a reference and a query file container; the second one focuses on the identification and classification of the source device brand, based on the analysis of containers structure and content. Noticeably, the latter application relies on the likelihood-ratio framework, which is more and more approved by the forensic community as the appropriate way to exhibit findings in court. We tested and proved the effectiveness of both applications on a dataset composed by 578 videos taken with modern smartphones from major brands and models. The proposed approaches are proved to be valuable also for requiring an extremely small computational cost as opposed to all available techniques based on the video stream analysis or manual inspection of file containers.

Forensic research community keeps proposing new techniques to analyze digital images and videos. However, the performance of proposed tools are usually tested on data that are far from reality in terms of resolution, source device, and processing history. Remarkably, in the latest years, portable devices became the preferred means to capture images and videos, and contents are commonly shared through social media platforms (SMPs, for example, Facebook, YouTube, etc.). These facts pose new challenges to the forensic community: for example, most modern cameras feature digital stabilization, that is proved to severely hinder the performance of video source identification technologies; moreover, the strong re-compression enforced by SMPs during upload threatens the reliability of multimedia forensic tools. On the other hand, portable devices capture both images and videos with the same sensor, opening new forensic opportunities. The goal of this paper is to propose the VISION dataset as a contribution to the development of multimedia forensics. The VISION dataset is currently composed by 34,427 images and 1914 videos, both in the native format and in their social version (Facebook, YouTube, and WhatsApp are considered), from 35 portable devices of 11 major brands. VISION can be exploited as benchmark for the exhaustive evaluation of several image and video forensic tools.

Recently, several studies have been concerned with modeling the parenthood relationships between near duplicates in a set of images. Two images share a parenthood relationship if one is obtained by applying transformations to the other. However, this is not the only form of parenting that can exist among images. An image might be a composition created through the combination of the semantic information existent in two or more source images, establishing a relationship between the sources and the composite. The problem of identifying these relations in a set containing near-duplicate subsets of source and composition images is referred to as multiple parenting phylogeny. Thus far, researchers tackled this problem with a three-step solution: 1) separation of near-duplicate groups; 2) classification of the relations between the groups; and 3) identification of the images used to create the original composition. In this work, we extend upon this framework by introducing key improvements, such as better identification of when two images share content, and improved ways to compare this content. In addition, we also introduce a new realistic professionally created data set of compositions involving multiple parenting relationships. The method we present in this paper is properly evaluated through quantitative metrics, established for assessing the accuracy in finding multiple parenting relationships. Finally, we discuss some particularities of the framework, such as the importance of an accurate reconstruction of phylogenies and the method's behavior when dealing with more complex compositions.

In this paper, we propose two different solutions for making a recently proposed asymmetric fingerprinting protocol based on client-side embedding robust to collusion attacks. The first solution is based on projecting a client-owned random fingerprint, securely obtained through existing cryptographic protocols, using for each client a different random matrix generated by the server. The second solution consists in assigning to each client a Tardos code, which can be done using existing asymmetric protocols, and modulating such codes using a specially designed random matrix. Suitable accusation strategies are proposed for both solutions, and their performance under the averaging attack followed by the addition of Gaussian noise is analytically derived. Experimental results show that the analytical model accurately predicts the performance of a realistic system. Moreover, the results also show that the solution based on independent random projections outperforms the solution based on Tardos codes, for different choices of parameters and under different attack models.

Image forensic analysis for the detection of contrast enhancement and other histogram-based processing, usually relies on the study of first-order statistics derived from image histogram. Methods based on such an approach, though, are easily circumvented by adopting some counter-forensic attacks. To overcome such a problem, we propose a novel forensic technique based on the study of second-order statistics derived from the co-occurrence matrix. The experiments we carried out demonstrate that the proposed approach is very effective even in the presence of counter-forensic attacks, while it retains the good performance of histogram-based methods when no attack is present.

In this paper, we propose a scheme to employ an asymmetric fingerprinting protocol within a client-side embedding distribution framework. The scheme is based on a novel client-side embedding technique that is able to transmit a binary fingerprint. This enables secure distribution of personalized decryption keys containing the Buyer's fingerprint by means of existing asymmetric protocols, without using a trusted third party. Simulation results show that the fingerprint can be reliably recovered by using non-blind decoding, and it is robust with respect to common attacks. The proposed scheme can be a valid solution to both customer's rights and scalability issues in multimedia content distribution.

We propose a method for detecting insertion and deletion of whole frames in digital videos. We start by strengthening and extending a state of the art method for double encoding detection, and propose a system that is able to locate the point in time where frames have been deleted or inserted, discerning between the two cases. The proposed method is applicable even when different codecs are used for the first and second compression, and performs well even when the second encoding is as strong as the first one.

Image phylogeny deals with tracing back parent-child relationships among near duplicates, images that share the same semantic content. This approach results in a visual structure showing the inheritance of semantic content among images, called phylogeny tree. In this paper, we extend upon the image phylogeny's original formulation, which considers that an image may inherit content from only a single parent, to deal with situations whereby an image may inherit it from multiple different parents. Our objective is to find the multiple parenting relationships in a set of images, a problem which we refer to as multiple parenting phylogeny. The proposed solution works by first identifying near-duplicate groups and reconstructing their phylogenies; then among the found groups we determine the one(s) representing the composition images; finally, we detect the parenting relations between those compositions and the source images used to create them.

Abstract In this work, by exploiting the traces left by double compression in the statistics of quantized modified discrete cosine transform coefficients, a single measure has been derived that allows to decide whether an MP3 file is singly or doubly compressed and, in the last case, to devise also the bit-rate of the first compression. Moreover, the proposed method as well as two state-of-the-art methods have been applied to analyze short temporal windows of the track, allowing the localization of possible tampered portions in the MP3 file under analysis. Experiments confirm the good performance of the proposed scheme and demonstrate that current detection methods are useful for tampering localization, thus offering a new tool for the forensic analysis of MP3 audio tracks.

In this paper, we propose a solution for implementing an asymmetric fingerprinting protocol within a client-side embedding distribution framework. The scheme is based on two novel client-side embedding techniques that are able to reliably transmit a binary fingerprint. The first one relies on standard spread-spectrum like client-side embedding, while the second one is based on an innovative client-side informed embedding technique. The proposed techniques enable secure distribution of personalized decryption keys containing the Buyer's fingerprint by means of existing asymmetric protocols, without using a trusted third party. Simulation results show that the fingerprint can be reliably recovered by using either nonblind decoding with standard embedding or blind decoding with informed embedding, and in both cases it is robust with respect to common attacks. To the best of our knowledge, the proposed scheme is the first solution addressing asymmetric fingerprinting within a client-side framework, representing a valid solution to both customer's rights and scalability issues in multimedia content distribution.

In this paper, a method to detect the presence of double compression in a MP3 audio file is proposed. By exploiting the effect of double compression in the statistical properties of quantized MDCT coefficients, a single measure is derived to decide if a MP3 file is single compressed or it has been double compressed and also to devise the bit-rate of the first compression. Experimental results confirm the performance of the detector, mainly when the bit-rate of the second compression is higher than the bit-rate of the first one.

A comparison between two forensic techniques for the reverse engineering of a chain composed by a double JPEG compression interleaved by a linear contrast enhancement is presented here. The first approach is based on the well known peak-to-valley behavior of the histogram of double-quantized DCT coefficients, while the second approach is based on the distribution of the first digit of DCT coefficients. These methods have been extended to the study of the considered processing chain, for both the chain detection and the estimation of its parameters. More specifically, the proposed approaches provide an estimation of the quality factor of the previous JPEG compression and the amount of linear contrast enhancement.

This work addresses forgery localization in MPEG-2 compressed videos. The proposed method is based on the analysis of Double Quantization (DQ) traces in frames that were encoded twice as intra (i.e., I-frames). Employing a state-of-the-art method, such frames are located in the video under analysis by estimating the size of the Group Of Pictures (GOP) that was used in the first compression; then, the DQ analysis is devised for the MPEG-2 encoding scheme and applied to frames that were intra-coded in both the first and second compression. In such a way, regions that were manipulated between the two encodings are detected. Compared to existing methods based on double quantization analysis, the proposed scheme makes forgery localization possible on a wider range of settings.

In this work, we present a decision fusion strategy for image forensics. We define a framework that exploits information provided by available forensic tools to yield a global judgment about the authenticity of an image. Sources of information are modeled and fused using Dempster-Shafer Theory of Evidence, since this theory allows us to handle uncertain answers from tools and lack of knowledge about prior probabilities better than the classical Bayesian approach. The proposed framework permits us to exploit any available information about tools reliability and about the compatibility between the traces the forensic tools look for. The framework is easily extendable: new tools can be added incrementally with a little effort. Comparison with logical disjunction- and SVM-based fusion approaches shows an improvement in classification accuracy, particularly when strong generalization capabilities are needed.

In this paper, a simple yet reliable algorithm to detect the presence of nonaligned double JPEG compression (NA-JPEG) in compressed images is proposed. The method evaluates a single feature based on the integer periodicity of the blockwise discrete cosine transform (DCT) coefficients when the DCT is computed according to the grid of the previous JPEG compression. Even if the proposed feature is computed relying only on DC coefficient statistics, a simple threshold detector can classify NA-JPEG images with improved accuracy with respect to existing methods and on smaller image sizes, without resorting to a properly trained classifier. Moreover, the proposed scheme is able to accurately estimate the grid shift and the quantization step of the DC coefficient of the primary JPEG compression, allowing one to perform a more detailed analysis of possibly forged images.

In this paper, we propose a forensic algorithm to discriminate between original and forged regions in JPEG images, under the hypothesis that the tampered image presents a double JPEG compression, either aligned (A-DJPG) or nonaligned (NA-DJPG). Unlike previous approaches, the proposed algorithm does not need to manually select a suspect region in order to test the presence or the absence of double compression artifacts. Based on an improved and unified statistical model characterizing the artifacts that appear in the presence of both A-DJPG or NA-DJPG, the proposed algorithm automatically computes a likelihood map indicating the probability for each 8 × 8 discrete cosine transform block of being doubly compressed. The validity of the proposed approach has been assessed by evaluating the performance of a detector based on thresholding the likelihood map, considering different forensic scenarios. The effectiveness of the proposed method is also confirmed by tests carried on realistic tampered images. An interesting property of the proposed Bayesian approach is that it can be easily extended to work with traces left by other kinds of processing.

In this paper, a forensic tool able to discriminate between original and forged regions in an image captured by a digital camera is presented. We make the assumption that the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to the demosaicking algorithm. The proposed method is based on a new feature measuring the presence of demosaicking artifacts at a local level, and on a new statistical model allowing to derive the tampering probability of each 2 × 2 image block without requiring to know a priori the position of the forged region. Experimental results on different cameras equipped with different demosaicking algorithms demonstrate both the validity of the theoretical model and the effectiveness of our scheme.

We propose a novel model to characterize the security of linear blinding techniques. The proposed model relates the security of blinding to the possibility of estimating the blinded signals up to a certain signal-to-noise ratio (SNR). Practical upper bounds on the SNR are derived by relying on rate-distortion theory and evaluating the mutual information between the blinded and the plaintext signals. The proposed bounds allow to characterize the security of different blinding techniques, showing that multiplicative blinding techniques can not achieve the same level of security as additive ones. The proposed model provides a rigorous measure for evaluating the tradeoff between security and efficiency in practical secure signal processing algorithms.

In this paper, we propose a statistical test to discriminate between original and forged regions in JPEG images, under the hypothesis that the former are doubly compressed while the latter are singly compressed. New probability models for the DCT coefficients of singly and doubly compressed regions are proposed, together with a reliable method for estimating the primary quantization factor in the case of double compression. Based on such models, the probability for each DCT block to be forged is derived. Experimental results demonstrate a better discriminating behavior with respect to previously proposed methods.

In this paper, we propose a simple yet reliable method to detect the presence of non-aligned double JPEG compression (NA-JPEG). The method is based on a single feature which depends on the integer periodicity of the DCT coefficients when the DCT is computed according to the grid of the previous JPEG compression. Even if the proposed feature is computed relying only on DC coefficient statistics, a simple threshold detector can classify NA-JPEG images with improved accuracy with respect to existing methods and on smaller image sizes. Moreover, the proposed method is able to accurately estimate the quantization step and the grid shift of the primary JPEG compression, which can be used to perform a more detailed analysis of possibly forged images.

Signal processing tools working directly on encrypted data could provide an efficient solution to application scenarios where sensitive signals must be protected from an untrusted processing device. In this paper, we consider the data expansion required to pass from the plaintext to the encrypted representation of signals, due to the use of cryptosystems operating on very large algebraic structures. A general composite signal representation allowing us to pack together a number of signal samples and process them as a unique sample is proposed. The proposed representation permits us to speed up linear operations on encrypted signals via parallel processing and to reduce the size of the encrypted signal. A case study-1-D linear filtering-shows the merits of the proposed representation and provides some insights regarding the signal processing algorithms more suited to work on the composite representation.

Client-side watermark embedding systems have been proposed as a possible solution for the copyright protection in large-scale content distribution environments. In this framework, we propose a new look-up-table-based secure client-side embedding scheme properly designed for the spread transform dither modulation watermarking method. A theoretical analysis of the detector performance under the most known attack models is presented and the agreement between theoretical and experimental results verified through several simulations. The experimental results also prove that the advantages of the informed embedding technique in comparison to the spread-spectrum watermarking approach, which are well known in the classical embedding schemes, are preserved in the client-side scenario. The proposed approach permits us to successfully combine the security of client-side embedding with the robustness of informed embedding methods.

Buyer-seller watermarking (BSW) protocols allow copyright protection of digital content. The protocol is anonymous when the identity of buyers is not revealed if they do not release pirated copies. Existing BSW protocols are not provided with a formal analysis of their security properties. We employ the ideal-world/real-world paradigm to propose a formal security definition for copyright protection protocols, and we analyze an anonymous BSW protocol and prove that it fulfills our definition. Additionally, we implement the protocol and measure its efficiency.

Client side watermark embedding schemes have been proposed as a possible solution for the copyright protection in large scale content distribution environments. In this framework, we propose a look-up-table based secure embedding system, designed for the Spread Transform Dither Modulation (ST-DM) watermarking algorithm, that outperforms Spread Spectrum based systems.

Signal processing in the encrypted domain (s.p.e.d.) appears an elegant solution in application scenarios, where valuable signals must be protected from a possibly malicious processing device. In this paper, we consider the application of the Discrete Cosine Transform (DCT) to images encrypted by using an appropriate homomorphic cryptosystem. An s.p.e.d. 1-dimensional DCT is obtained by defining a convenient signal model and is extended to the 2-dimensional case by using separable processing of rows and columns. The bounds imposed by the cryptosystem on the size of the DCT and the arithmetic precision are derived, considering both the direct DCT algorithm and its fast version. Particular attention is given to block-based DCT (BDCT), with emphasis on the possibility of lowering the computational burden by parallel application of the s.p.e.d. DCT to different image blocks. The application of the s.p.e.d. 2D-DCT and 2D-BDCT to 8-bit greyscale images is analyzed; whereas a case study demonstrates the feasibility of the s.p.e.d. DCT in a practical scenario.

Signal-processing modules working directly on encrypted data provide an elegant solution to application scenarios where valuable signals must be protected from a malicious processing device. In this paper, we investigate the implementation of the discrete Fourier transform (DFT) in the encrypted domain by using the homomorphic properties of the underlying cryptosystem. Several important issues are considered for the direct DFT: the radix-2 and the radix-4 fast Fourier algorithms, including the error analysis and the maximum size of the sequence that can be transformed. We also provide computational complexity analyses and comparisons. The results show that the radix-4 fast Fourier transform is best suited for an encrypted domain implementation in the proposed scenarios.

Signal processing modules working directly on the encrypted data could provide an elegant solution to application scenarios where valuable signals should be protected from a malicious processing device. In this paper, we investigate the implementation of the discrete Fourier transform (DFT) in the encrypted domain, by using the homomorphic properties of the underlying cryptosystem. Several important issues are considered for both the DFT and radix-2 fast Fourier transform, including the error analysis and the maximum size of the sequence that can be transformed.

Processing a signal directly in the encrypted domain provides an elegant solution in application scenarios where valuable signals must be protected from a malicious processing device. In a previous paper we considered the implementation of the ID discrete fourier transform (DFT) in the encrypted domain, by using the homomorphic properties of the underlying cryptosystem. In this paper we extend our previous results by considering the application of the 2-dimensional DCT to encrypted images. The effect of the consecutive application of the DCT algorithm first by rows then by columns is considered, as well as the differences between the implementation of the direct DCT algorithm and its fast version. Particular attention is given to block-based DCT, with emphasis on the possibility of lowering the computational burden by parallel application of the encrypted domain DCT algorithm to different image blocks.

The processing and encryption of multimedia content are generally considered sequential and independent operations. In certain multimedia content processing scenarios, it is, however, desirable to carry out processing directly on encrypted signals. The field of secure signal processing poses significant challenges for both signal processing and cryptography research; only few ready-to-go fully integrated solutions are available. This study first concisely summarizes cryptographic primitives used in existing solutions to processing of encrypted signals, and discusses implications of the security requirements on these solutions. The study then continues to describe two domains in which secure signal processing has been taken up as a challenge, namely, analysis and retrieval of multimedia content, as well as multimedia content protection. In each domain, state-of-the-art algorithms are described. Finally, the study discusses the challenges and open issues in the field of secure signal processing.

The problem of secure data processing by means of a neural network (NN) is addressed. Secure processing refers to the possibility that the NN owner does not get any knowledge about the processed data since they are provided to him in encrypted format. At the same time, the NN itself is protected, given that its owner may not be willing to disclose the knowledge embedded within it. The considered level of protection ensures that the data provided to the network and the network weights and activation functions are kept secret. Particular attention is given to prevent any disclosure of information that could bring a malevolent user to get access to the NN secrets by properly inputting fake data to any point of the proposed protocol.With respect to previous works in this field, the interaction between the user and the NN owner is kept to a minimum with no resort to multiparty computation protocols.

The break our watermarking system (BOWS) contest was launched in the framework of the activities carried out by the European Network of Excellence for Cryptology ECRYPT. The aim of the contest was to investigate how and when an image watermarking system can be broken while preserving the highest possible quality of the content, in the case the watermarking system is subject to a massive worldwide attack. The great number of participants and the echo that the contest has had in the watermarking community contributed to make BOWS a great success. From a scientific point of view, many insights into the problems attackers have to face with when operating in a practical scenario have been obtained, confirming the threat posed by the sensitivity attack, which turned out to be the most successful attack. At the same time, several interesting modifications of such an attack have been proposed to make it work in a real scenario under limited communication and time resources. This paper describes how the contest has been designed and analyzes the general progress of the attacks during the contest.

Article ID 82790

In art diagnostics it is often needed to compare and integrate different sets of information, coming from different sources, and stored in different images. In order to successfully integrate these data, images corresponding to the same areas need to be registered, that is a geometrical transformation that aligns points in one picture with corresponding points in another picture needs to be found and applied. In this paper we present an automatic registration technique for multispectral images, based on the computation of mutual information. We applied this method to obtain RGB images from multispectral data, and derive a (undersampled) spectral signature per pixel of the painting.

Recently, data hiding has been proposed to improve the performance of error concealment algorithms. In this paper, a new data hiding-based error concealment algorithm is proposed, that allows the increase of video quality in H.264/AVC wireless video transmission and real-time applications. Data hiding is used for carrying to the decoder the values of some inner pixels to be used to reconstruct lost macro blocks into intra frames through a bi-linear interpolation process.

Abstract Advances in electronic imaging over recent years have encouraged the development of new tools for cultural heritage applications. In this paper a software application called ArtShop is described, containing some tools for artwork image restoration, developed during several years of research at the Image and Communications Laboratory of the University of Florence. Copyright © 2003 John Wiley & Sons, Ltd.

In the field of image watermarking, research has been mainly focused on grayscale image watermarking, whereas the extension to the color case is usually accomplished by marking the image luminance, or by processing each color channel separately. A DCT domain watermarking technique expressly designed to exploit the peculiarities of color images is presented. The watermark is hidden within the data by modifying a subset of full-frame DCT coefficients of each color channel. Detection is based on a global correlation measure which is computed by taking into account the information conveyed by the three color channels as well as their interdependency. To ultimately decide whether or not the image contains the watermark, the correlation value is compared to a threshold. With respect to existing grayscale algorithms, a new approach to threshold selection is proposed, which permits reducing the probability of missed detection to a minimum, while ensuring a given false detection probability. Experimental results, as well as theoretical analysis, are presented to demonstrate the validity of the new approach with respect to algorithms operating on image luminance only.

The peculiarities of digital map images are exploited to develop a watermarking algorithm which is robust against geometric distortions. Robustness against geometric attacks is achieved through text-based image normalization. First, text is extracted from the to-be-marked map, then text orientation and size are exploited to normalize the image geometry prior to watermark insertion. Watermarking is performed by means of any of the existing algorithms ensuring good robustness against image processing tools. At the decoder side, text is extracted again from the map and used to normalize image geometry. Owing to the robustness of text features with respect to common image manipulations, and to the likely spreading of text all across the digital map, the proposed system exhibits an excellent robustness, as is witnessed by the experimental results reported.

This paper deals with robust watermarking of cartographic images. We present a method (text-based geometric normalization-TBGN) which, by exploiting the particular content of cartographic images, namely text content, permits one to cope with global geometric transformations. The validity of the method is validated by experimental results.

The performance of syntax-based error detection in the framework of H.263 video transmission is not sufficient to detect errors reliably, calling for the adoption of more effective error detection techniques. A new technique, based on data hiding concepts, is proposed here to increase the error detection rate in H.263 video sequences transmitted over error-prone communication channels. Some information is embedded into the compressed H.263 video stream aimed at highlighting the regions of the frame corrupted by transmission errors. The method preserves the image quality and maintains the original bit rate, without any modifications of the H.263 standard. The proposed approach allows one to avoid the transmission overhead typical of FEC-based or header-based error detection algorithms, and the extra computational burden peculiar to image analysis-based techniques, achieving better results with respect to syntax-based error detection, thanks to the use of side information at the decoder.

A watermarking algorithm operating in the wavelet domain is presented. Performance improvement with respect to existing algorithms is obtained by means of a new approach to mask the watermark according to the characteristics of the human visual system (HVS). In contrast to conventional methods operating in the wavelet domain, masking is accomplished pixel by pixel by taking into account the texture and the luminance content of all the image subbands. The watermark consists of a pseudorandom sequence which is adaptively added to the largest detail bands. As usual, the watermark is detected by computing the correlation between the watermarked coefficients and the watermarking code, and the detection threshold is chosen in such a way that the knowledge of the watermark energy used in the embedding phase is not needed, thus permitting one to adapt it to the image at hand. Experimental results and comparisons with other techniques operating in the wavelet domain prove the effectiveness of the new algorithm.

Watermark detection, i.e., the detection of an invisible signal hidden within an image for copyright protection or data authentication, has classically been tackled by means of correlation-based techniques. Nevertheless, when watermark embedding does not obey an additive rule, or when the features the watermark is superimposed on do not follow a Gaussian pdf, correlation-based decoding is not the optimum choice. A new decoding algorithm is presented here which is optimum for nonadditive watermarks embedded in the magnitude of a set of full-frame DFT coefficients of the host image. By relying on statistical decision theory, the structure of the optimum is derived according to the Neyman-Pearson criterion, thus permitting to minimize the missed detection probability subject to a given false detection rate. The validity of the optimum decoder has been tested thoroughly to assess the improvement it permits to achieve from a robustness perspective. The results we obtained confirm the superiority of the novel algorithm with respect to classical correlation-based decoding.

So far digital watermarking has been indicated as the most feasible answer for multimedia copyright protection issues, though many problems, especially regarding aspects of robustness against geometrical attacks, have not been completely and adequately solved yet. Robustness against geometric manipulations has been dealt with by inserting, together with the watermark, a synchronization template to be used later in the detection phase, to determine if a geometric distortion occurred and invert it before looking for the mark. A novel technique is presented, which, by exploiting the theory of geometric invariants, inserts a watermark intrinsically resistant to this sort of manipulations, thus avoiding the need of a synchronization pattern. Preliminary experimental results proving the goodness of the methodology are discussed along with some implementation problems due to the computational complexity.

The MPEG-4 standard is revealing very attractive for a large set of applications. In some of them a copy protection system allowing to control the distribution of multimedia data is required. A new technology useful for copyright protection is watermarking: a digital code (watermark), indicating the copyright owner, is directly embedded into the video signal. The possibility of the MPEG-4 standard to directly access objects within a video sequence introduces a constraint to the watermarking process: even if a video object is transferred from a sequence to another, the copyright data of the single object has to be correctly detected. Another requirement is that, in order to be robust against format conversions, the watermark has to be inserted before compression. The method proposed in this paper satisfies the previous requirements by relying on an image watermarking algorithm which embeds the code in the discrete wavelet transform of each frame.

The evaluation of the number of bits that can be hidden within an image through digital watermarking is a crucial topic, which has been addressed only for additive watermarks. The evaluation of watermark capacity is very important because it allows to put a theoretical upper bound on the amount of information that can be hidden into an image by a given watermarking procedure, regardless of the watermark extraction technique. It is the purpose of this work to suggest a methodology for the evaluation of the watermark capacity in a nonadditive, non-Gaussian framework, and to discuss the results we obtained by applying it to a set of standard images.

In the last few years, digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia data against unauthorized uses. In the field of image watermarking, research has been mainly focused on grey-scale image watermarking, whereas the extension to the color case is usually accomplished by marking the image luminance, or by processing each color channel separately. In this paper, a DCT domain technique expressly devised for watermarking of color images is presented, which exploits the characteristics of the human visual system and the correlation between the RGB image channels. Experimental results are presented to demonstrate the validity of the new approach with respect to algorithms operating on image luminance only.

The interest in image watermarking techniques has rapidly grown during the years. Two requirements needed to be satisfied to use watermarking techniques for copyright protection are: unperceivability and robustness against image processing algorithms and forgery attacks. In particular, it is widely accepted that the exploitation of the characteristics of the human visual system should greatly help in satisfying both these requirements. Some solutions to the problem of building some perceptual masks for better hiding watermarks embedded in the full-frame DCT domain are presented. The results support the validity of the approach.

Digital watermarking has been proposed as a viable solution to the need of copyright protection and authentication of multimedia data in a networked environment, since it makes it possible to identify the author, owner, distributor or authorized consumer of a document. In this paper a new watermarking technique to add a code to digital images is presented; the method operates in the frequency domain embedding a pseudo-random sequence of real numbers in a selected set of DCT coefficients. Watermark casting is performed by exploiting the masking characteristics of the human visual system, to ensure watermark invisibility. The embedded sequence is extracted without resorting to the original image, so that the proposed technique represents a major improvement to methods relying on the comparison between the watermarked and original images. Experimental results demonstrate that the watermark is robust to most of the signal processing techniques and geometric distortions.