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In this work the use of different metrics in vector median filters is investigated. The impact of the metric is considered from the point of view of both performance and algorithm complexity. In order to achieve more intuitive results a geometrical approach is adopted. Finally a fast approximated algorithm for the L1 case is introduced.

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 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.

Research on data hiding is demonstrating every day that several applications can benefit from this technology; among these, medical data management. In particular, embedding patient information into a medical image through data hiding could improve the level of security and confidentiality that is essential for the diffusion of medical information systems. The design of a data hiding system for such an application has to take into account specific requirements, the most important are: high payload to identifying reliably a patient; quality preservation of the watermarked image; robustness to content modification. According to this analysis, a comparison between different data hiding approaches is presented, to evaluate the most suitable algorithms for embedding patient information into digital radiographs. In particular two algorithms based on statistical decision theory have been compared with schemes following the new approach of modelling data hiding as communication with side-information at the transmitter. These methods have been tested and compared in the framework of digital radiographies management in order to identify their benefits and drawbacks.

Among the techniques to limit the effect of error propa- gation in low bitrate video coding, the best performance is achieved through the joint use of forward error correction (FEC) and auto- matic repeat request strategies. However, retransmission of cor- rupted data introduces additional delay which may be critical in some practical applications. In such cases, only a FEC strategy is feasible at the expense of a significant increase of the overall trans- mission bit rate. In this paper, a strategy is proposed which instead of protecting the whole H.263 video stream, uses all the redundancy to protect the most important parts of the bit stream, i.e., group-of- block (GOB) start codes. More specifically, the 22-bit long start code used in the H.263 standard is replaced with longer Gold sequences which ensure a higher protection against noise. A different se- quence is used for each GOB so that additional information can be obtained which can be used to improve the quality of the decoded sequence. The new technique is derived by assuming a slow-fading channel such as those encountered in pedestrian applications. Simulation results prove the effectiveness of the proposed tech- nique with respect to classical FEC schemes in terms of PSNR and overall bit rate. © 2001 SPIE and IS&T. (DOI: 10.1117/1.1407262)

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.

In this paper we present a new method for the detection of forgeries in digital videos, using the sensor's pattern noise. The camera pattern noise is a unique stochastic high frequency characteristic of imaging sensors and the detection of a forged frame in a video is determined by comparing the correlation between the noise within the frame itself and the reference pattern noise with an empirical threshold. The reference pattern is created for the identification of the camera and the authentication of the video too. Such a pattern is defined as self building because it is created from the video sequence during the time develop, with a technique applied frame per frame, by averaging the noise extracted from each frame. The method has been inherited from an existing system created by Fridrich et al.1 for still images. By using this method we are able to identify if all the scenes of a video sequence have been taken with the same camera and if the number and/or the content of the frames of the video have been modified. A large section of the paper is dedicated to the experimental results, where we demonstrate that it is possible to perform a reliable identification even from video that has undergone MPEG compression or frame interpolation.

A new technique is described which couples median filtering and image deblurring techniques to filter noisy images without introducing defocusing side effects. To deal with colour images a vector median filtering procedure is proposed. Using this procedure a better edge preserving filter is obtained which does not introduce new colours. The deblurring operation is performed componentwise by fitting an ARMA model to the image. The AR part of the model estimates the image and the MA part estimates and blurring function. Finally the MA part is inverted and applied to remove the blur introduced by the median filter.

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.

International audience; In an increasing number of image and video processing problems, cryptographic techniques are used to enforce content access control, identity verification and authentication, and privacy protection. The combination of cryptography and signal processing is an exciting emerging field. This introductory paper gives an overview of approaches and challenges that exist in applying cryptographic primitives to important image and video processing problems, including (partial) content encryption, secure face recognition, and secure biometrics. This paper aims to help the community in appreciating the utility and challenges of cryptographic techniques in image and video processing.

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.

A novel quantization-based data-hiding method, named rational dither modulation (RDM), is presented. This method amounts to simple modifications of the well-known dither modulation (DM) scheme, which is largely vulnerable to scaling attacks. With such modifications, RDM becomes invariant to those attacks. Since RDM does not work by trying to estimate the step-size of the quantizers, it does not need any pilot-sequence. Moreover, RDM is suitable for a scalar operation, thus avoiding the cumbersome constructions of spherical codes. It is also shown that RDM approaches the performance of DM asymptotically with the size of the memory needed for the method to operate. Simulation results show the accuracy of our theoretical analysis and the superiority of RDM compared to the improved spread spectrum method.

Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia data in a networked environment. It makes possible to tightly associate to a digital document a code allowing the identification of the data creator, owner, authorized consumer, and so on. In this paper a new watermarking algorithm for digital images is presented: the method, which operates in the frequency domain, embeds a pseudo-random sequence of real numbers in a selected set of DCT coefficients. After embedding, the watermark is adapted to the image by exploiting the masking characteristics of the human visual system, thus ensuring the watermark invisibility. By exploiting the statistical properties of the embedded sequence, the mark can be reliably extracted without resorting to the original uncorrupted image. Experimental results demonstrate that the watermark is robust to several signal processing techniques, including JPEG compression, low pass and median filtering, histogram equalization and stretching, dithering, addition of Gaussian noise, resizing, and multiple watermarking.

Image watermarking has received great attention. Nevertheless, researchers are still faced with several problems: the extension of gray-level watermarking to the color case is one of them. To get rid of the problems raised by the correlation among image color bands, a new approach based on the decorrelation property of the Karhunen-Loeve transform (KLT) is proposed. The KLT is first applied to the RGB components of the image, then watermarking is performed independently in the DFT domain of the KL-transformed bands. Watermark embedding is achieved by modifying the magnitude of mid-frequency DFT coefficients according to an additive-multiplicative rule. On detection, KL decorrelation is exploited to optimally detect the watermark presence by relying on Bayes statistical decision theory.

Digital watermarking has been proposed as a solution to the problem of copyright protection of multimedia documents in a networked environment. It makes it possible to tightly associate a code to an image, allowing the identification of the data creator, owner and so on. In this paper, after a review of some important watermarking techniques, a new watermarking algorithm for digital images is presented: the method, which enhances a previous technique proposed by Cox et al. [1] [I.J. Cox, J. Kilian, T. Leighton, T. Shamoon, Secure spread spectrum watermarking for multimedia, NEC Research Institute Technical Report 95-10], operates in the frequency domain, by embedding in a selected set of DCT coefficients a sequence of randomly generated real numbers. By exploiting their statistical characteristics, it is possible to reliably extract the embedded sequences without resorting to the original unmarked image. Experimental results demonstrate that the watermark is robust to several image transformations.

A new classification of digital watermarking techniques, which is based on the way the decoding process works, is introduced. This classification is used to analyze the impact concepts such as watermark blindness, privateness, readability and reversibility have on practical application scenarios. In the second part of the work, a new, object-oriented, electronic copyright management system (ECMS) is presented which, by relying on state-of-the-art technology, makes it possible the enforcement of copyright laws in an open-network environment.

Recent studies exposed the weaknesses of scale-invariant feature transform (SIFT)-based analysis by removing keypoints without significantly deteriorating the visual quality of the counterfeited image. As a consequence, an attacker can leverage on such weaknesses to impair or directly bypass with alarming efficacy some applications that rely on SIFT. In this paper, we further investigate this topic by addressing the dual problem of keypoint removal, i.e., the injection of fake SIFT keypoints in an image whose authentic keypoints have been previously deleted. Our interest stemmed from the consideration that an image with too few keypoints is per se a clue of counterfeit, which can be used by the forensic analyst to reveal the removal attack. Therefore, we analyse five injection tools reducing the perceptibility of keypoint removal and compare them experimentally. The results are encouraging and show that injection is feasible without causing a successive detection at SIFT matching level. To demonstrate the practical effectiveness of our procedure, we apply the best performing tool to create a forensically undetectable copy-move forgery, whereby traces of keypoint removal are hidden by means of keypoint injection.

Copy-move forgeries are very common image manipulations that are often carried out with malicious intents. Among the techniques devised by the ‘Image Forensic’ community, those relying on scale invariant feature transform (SIFT) features are the most effective ones. In this paper, we approach the copy-move scenario from the perspective of an attacker whose goal is to remove such features. The attacks conceived so far against SIFT-based forensic techniques implicitly assume that all SIFT keypoints have similar properties. On the contrary, we base our attacking strategy on the observation that it is possible to classify them in different typologies. Also, one may devise attacks tailored to each specific SIFT class, thus improving the performance in terms of removal rate and visual quality. To validate our ideas, we propose to use a SIFT classification scheme based on the gray scale histogram of the neighborhood of SIFT keypoints. Once the classification is performed, we then attack the different classes by means of class-specific methods. Our experiments lead to three interesting results: (1) there is a significant advantage in using SIFT classification, (2) the classification-based attack is robust against different SIFT implementations, and (3) we are able to impair a state-of-the-art SIFT-based copy-move detector in realistic cases.

Digital watermarking has been proposed as a means to protect the copyright of multimedia data in a networked environment, since it makes it possible to tightly embed a code into a digital document allowing the identification of the data owner. A new watermarking system for digital images is presented: the method embeds a sequence of random real numbers in a selected set of DCT coefficients. Embedding is performed by exploiting the masking characteristics of the human visual system, to ensure the watermark invisibility. The embedded sequence can be extracted without resorting to the original uncorrupted image. Experimental results demonstrate that the watermark is robust to several signal processing techniques and geometric distortions.

This paper deals with the application of distributed source coding (DSC) theory to remote sensing image compression. Although DSC exhibits a significant potential in many application fields, up till now the results obtained on real signals fall short of the theoretical bounds, and often impose additional system-level constraints. The objective of this paper is to assess the potential of DSC for lossless image compression carried out onboard a remote platform. We first provide a brief overview of DSC of correlated information sources. We then focus on onboard lossless image compression, and apply DSC techniques in order to reduce the complexity of the onboard encoder, at the expense of the decoder's, by exploiting the correlation of different bands of a hyperspectral dataset. Specifically, we propose two different compression schemes, one based on powerful binary error-correcting codes employed as source codes, and one based on simpler multilevel coset codes. The performance of both schemes is evaluated on a few AVIRIS scenes, and is compared with other state-of-the-art 2D and 3D coders. Both schemes turn out to achieve competitive compression performance, and one of them also has reduced complexity. Based on these results, we highlight the main issues that are still to be solved to further improve the performance of DSC-based remote sensing systems.

The use of machine-learning for multimedia forensics is gaining more and more consensus, especially due to the amazing possibilities offered by modern machine learning techniques. By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceeds those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques. However, the inherent vulnerability and fragility of machine learning architectures pose new serious security threats, hindering the use of these tools in security-oriented applications, and, among them, multimedia forensics. The analysis of the security of machine learning-based techniques in the presence of an adversary attempting to impede the forensic analysis, and the development of new solutions capable to improve the security of such techniques is then of primary importance, and, recently, has marked the birth of a new discipline, named Adversarial Machine Learning. By focusing on Image Forensics and image manipulation detection, in particular, this thesis contributes to the above mission by developing novel techniques for enhancing the security of binary manipulation detectors based on machine learning in several adversarial scenarios. The validity of the proposed solutions has been assessed by considering several manipulation tasks, ranging from the detection of double compression and contrast adjustment to the detection of geometric transformations and filtering operations., Cite1: https://www.researchgate.net/publication/ 340402438_Machine_Learning_Techniques_for_Image_Forensics_in_Adversarial_Setting ***** Cite2: https://www.sciencedirect.com/science/article/pii/S0167404820303655

International audience; This paper deals with the improvement of the Dirty Paper Trellis Code (DPTC) watermarking algorithm. This watermarking algorithm is known to be one of the best among the high rate watermarking schemes. Nevertheless, recent researches reveal its security weakness. Previously, we proposed to reinforce its security by using a secret space before the embedding. This secret space requires to compute projections onto secrets carriers. When dealing with high rate watermarking, the CPU cost for those projections is dramatically high. After introducing the watermarking scheme, we then propose two Space Division Multiplexing (SDM) approaches which reduce the complexity. Evaluations are achieved with four different attacks and show that our proposal gives better robustness results with SDM approaches.