The forensic analysis of resampling traces in upscaled images is addressed via subspace decomposition and random matrix theory principles. In this context, we derive the asymptotic eigenvalue distribution of sample autocorrelation matrices corresponding to genuine and upscaled images. To achieve this, we model genuine images as an autoregressive random field and we characterize upscaled images as a noisy version of a lower dimensional signal. Following the intuition behind Marčenko-Pastur law, we show that for upscaled images, the gap between the eigenvalues corresponding to the low-dimensional signal and the ones from the background noise can be enhanced by extracting a small number of consecutive columns/rows from the matrix of observations. In addition, using bounds provided by the same law for the eigenvalues of the noise space, we propose a detector for exposing traces of resampling. Finally, since an interval of plausible resampling factors can be inferred from the position of the gap, we empirically demonstrate that by using the resulting range as the search space of existing estimators (based on different principles), a better estimation accuracy can be attained with respect to the standalone versions of the latter.

Dynamic Information Flow Tracking (DIFT) is a technique for tracking information as it flows through a program's execution. DIFT systems track information by tainting data and propagating the taint marks throughout execution. These systems are designed to have minimal overhead and thus often miss indirect flows. If indirect flows were propagated naively overtainting would result, whereas propagating them effectively causes overhead. We describe the design and evaluation of a system intended for offline analysis, such as reverse engineering, that can track information through indirect flows. Our system, V-DIFT, uses a vector of floating point values for each taint mark. The use of vectors enables us to track a taint's provenance and handle indirect flows, trading off some performance for these abilities. These indirect flows via control and address dependencies are thought to be critical to tracking information flow of cryptographic programs. Therefore we tested V-DIFT's effectiveness by automatically locating keys in simple programs that use a variety of symmetric cryptographic algorithms found in three common libraries. This application does not require that the program run in real time, just that it be much faster than a manual approach. Our V-DIFT implementation tests average 3.6 seconds, and with the right parameters can identify memory locations that contain keys for 24 out of 27 algorithms tested. Our results show that many cryptographic algorithm implementations' address and/or control dependencies must be tracked for DIFT to be effective.

Two sequential camera fingerprint detection methods are proposed. Sequential tests implement a log-likelihood ratio test in an incremental way, thus enabling a reliable decision with a minimal number of observations. One of our methods adapts Goljan et al.'s to sequential operation. The second, which offers better performance in terms of average number of test observations, is based on treating the alternative hypothesis as a doubly stochastic model. Finally, we validate the performance of our methods with experiments and compare them with the state of the art in fast camera fingerprint detection.

Presents a listing of the new Associate Editors for this issue of the publication.

Powerful image editing software is nowadays capable of creating sophisticated and visually compelling fake photographs, thus posing serious issues to the trustworthiness of digital contents as a true representation of reality. Digital image forensics has emerged to help regain some trust in digital images by providing valuable aids in learning the history of an image. Unfortunately, in real scenarios, its application is limited, since multiple processing operators are likely to be applied, which alters the characteristic footprints exploited by current forensic tools. In this paper, we develop a novel forensic technique that is able to detect chains of operators applied to an image. In particular, we study the combination of Joint Photographic Experts Group compression and full-frame linear filtering, and derive an accurate mathematical framework to fully characterize the probabilistic distributions of the discrete cosine transform (DCT) coefficients of the quantized and filtered image. We then exploit such knowledge to define a set of features from the DCT distribution and build an effective classifier able to jointly disclose the quality factor of the applied compression and the filter kernel. Extensive experimental analysis illustrates the efficiency and versatility of the proposed approach, which effectively overcomes the state-of-the-art.

A novel complex flat fading channel estimation scheme is proposed. Contrarily to previous schemes in the literature, this new approach is not based on introducing pilot sequences, but on reducing the interference caused by the information-bearing signal on the estimation-aiding signal by using Dirty Paper Coding. We show through simulations that our method outperforms the Partially-Data Dependent scheme, which is a state-of-the-art technique based on superimposed pilots.

First-order statistics of First Significant Digits (FSD) have been recently exploited in multimedia forensics as a powerful tool to reveal traces of previous coding operations. As an answer, adversarial approaches aimed at modifying the FSD histogram and fooling such forensic methods have been proposed. However, the existing techniques have limitations in terms of distortion introduced in the multimedia object. In this paper, a transportation-theoretic formulation of the problem is presented which provides a close-to-optimal solution. Such strategy is tested in a well-known image forensic scenario, where FSDs of 8 × 8-DCT coefficients after single or double quantization are modified in order to restore a certain target histogram and the distortion with respect to the provided compressed image is measured in terms of MSE.

Watermarking schemes based on the Dirty Paper Coding (DPC) paradigm have been shown to achieve much higher rates than classical Spread-Spectrum methods. However, in practice, the latter continue to be used due to their higher security and robustness. In fact, the most prevalent DPC method, the so-called Scalar Costa Scheme (SCS), is prone to non-additive attacks, such as a simple gain which produces a desynchronization between the embedding and decoding codebooks thus severely affecting performance. Although some gain-robust modifications to the basic SCS exist, all have serious drawbacks. One alternative, which was somehow abandoned for its complexity, is to estimate the gain at the decoder, with the advantage of preserving the simplicity of SCS. In this paper we take a new look at the estimation problem and propose an affordable algorithm to perform Maximum Likelihood estimation of the channel gain, that is able to restore the original SCS performance. We also show and experimentally illustrate how our scheme can be effectively adapted to watermark decoding in filtered images.

There has been a recent interest in counterforensics as an adversarial approach to forensic detectors. Most of the existing counterforensics strategies, although successful, are based on heuristic criteria, and their optimality is not proven. In this paper the optimal modification strategy of a content in order to fool a histogram-based forensics detector is derived. The proposed attack relies on the assumption of a convex cost function; special attention is paid to the Euclidean norm, obtaining the optimal attack in the MSE sense. In order to prove the usefulness of the proposed strategy, we employ it to successfully attack a well-known algorithm for detecting double JPEG compression.

The most widely used information lossy source coding schemes for multimedia signals rely on the quantization of the content samples in a linearly transformed domain. A number of forensic applications (e.g., processing history estimation, tampering detection, software identification) can be posed as the estimation of the equivalent lattice quantizer from the observed samples. We present a new lattice estimation algorithm based on the observation of noisy points of the lattice. Although inspired by the work of Neelamani et al., our scheme uses the so-called “dual lattice” to achieve significant performance improvements with respect to its predecessors as measured by the number of vectors of the lattice basis that can be correctly estimated. Such performance improvement is even more dramatic when small pieces of the contents are considered, which indeed is especially relevant for forensic applications.

The characteristic artifacts left in an image by JPEG compression are often exploited to gather information about the processing history of the content. However, linear image filtering, often applied as postprocessing to the entire image (full-frame) for enhancement, may alter these forensically significant features, thus complicating the application of the related forensics techniques. In this paper, we study the combination of JPEG compression and full-frame linear filtering, analyzing their impact on the Discrete Cosine Transform (DCT) statistical properties of the image. We derive an accurate mathematical framework that allows to fully characterize the probabilistic distributions of the DCT coefficients of the quantized and filtered image. We then exploit this knowledge to estimate the applied filter. Experimental results show the effectiveness of the proposed method.

In a wide range of practical multimedia scenarios several correlated contents are available. The aim of this work is to quantify the gain that can be achieved in forensic applications by jointly considering those contents, instead of analyzing them separately. The used tool is the Kullback-Leibler Divergence between the distributions corresponding to different operators; the Maximum Likelihood estimator of the applied operator is also obtained, in order to illustrate how the correlation is exploited for estimation. Our detailed analysis is constrained to the Gaussian case (both for the input signal distribution and the processing randomness) and linear operators. Several practical scenarios are studied, and the relationships between the derived results are established. Finally, the links with Distributed Source Coding are highlighted.

In this work Weber's law is followed for designing a perceptually shaped side-informed data hiding scheme. The resulting method is a generalized version of a logarithmic quantization algorithm previously proposed by the authors. Closed formulas for analyzing the embedding power and decoding error probability of this new method are provided, and experimental results showing its good behavior against severe attacks are reported.

The problem of optimum watermark embedding and detection was addressed in a recent paper by Merhav and Sabbag, where the optimality criterion was the maximum false-negative error exponent subject to a guaranteed false-positive error exponent. In particular, Merhav and Sabbag derived universal asymptotically optimum embedding and detection rules under the assumption that the detector relies solely on second-order joint empirical statistics of the received signal and the watermark. In the case of a Gaussian host signal and a Gaussian attack, however, closed-form expressions for the optimum embedding strategy and the false-negative error exponent were not obtained in that work. In this paper, we derive the false-negative error exponent for any given embedding strategy and use such a result to show that in general the optimum embedding rule depends on the variance of the host sequence and the variance of the attack noise. We then focus on high signal-to-noise ratio (SNR) regime, deriving the optimum embedding strategy for such a setup. In this case, a universally optimum embedding rule turns out to exist and to be very simple with an intuitively appealing geometrical interpretation. The effectiveness of the newly proposed embedding strategy is evaluated numerically.

Privacy and security have always been key concerns for individuals. They have also been closely related concepts: in order to increase their perception of security, people sacrifice a part of their privacy by accepting to be surveilled by others. The tradeoff between both is usually reasonable and commonly accepted; however, the case of videosurveillance systems has been particularly controversial since their inception, as their benefits are not perceived to compensate for the privacy loss in many cases. The situation has become even worse during the last years with the massive deployment of these systems, which often do not provide satisfactory guarantees for the citizens. This paper proposes a DRM-based framework for videosurveillance to achieve a better balance between both concepts: it protects privacy of the surveilled individuals, whilst giving support to efficient automated surveillance.

A performance analysis of the authentication method proposed by J. Fridrich and M. Goljan is carried out. This method has the particular feature that both the embedder and the detector generate the watermark from a perceptual digest of the image. Hence, in order to accurately analyze the performance, the digest errors caused by the watermark embedding, the addition of a complementary signal and the scaling attacks are also taken into account.

This paper analyzes the performance of the image authentication method based on robust hashing proposed by J. Fridrich and M. Goljan . In this method, both the embedder and the detector generate the watermark from a perceptual digest of the image. Therefore, an accurate performance analysis requires assessing the relation between noise and hash bit errors. Our approach first derives the probability of hash bit error due to watermark embedding and/or the attack, and then uses such probability to derive the probabilities of false positive and false negative.

In this paper the authors analyze how the description and presentation of results about an algorithm proposed in the literature should be modified in order to comply with the reproducible signal processing paradigm. We describe the problems one is faced with, by specifically focusing on how the description of the algorithm should be improved with respect to the classical approach.

Scaling attacks are well-known to be some of the most harmful strategies against quantization-based watermarking methods, as they can completely ruin the performance of the watermarking system with almost no perceptual impact on the watermarked signal. In this paper we propose a new family of quantization-based methods specifically devised to deal with those attacks, and which presents the desirable property of yielding perceptually shaped watermarks.

Gabor filters are biologically motivated convolution kernels that have been widely used in the field of computer vision and, specially, in face recognition during the last decade. This paper proposes a statistical model of Gabor coefficients extracted from face images using generalized Gaussian distributions (GGD's). By measuring the Kullback-Leibler distance (KLD) between the pdf of the GGD and the relative frequency of the coefficients, we conclude that GGD's provide an accurate modeling. The underlying statistics allow us to reduce the required amount of data to be stored (i.e. data compression) via Lloyd-Max quantization. Verification experiments on the XM2VTS database show that performance does not drop when, instead of the original data, we use quantized coefficients.

Scalar spread transform dither modulation (SSTDM), together with its variants, is one of the foremost side-informed data hiding methods. Because of the projection stage of additive spread spectrum (Add-SS), SSTDM achieves the same spreading gain as the former, so that both become robust to additive attacks. We show that the similarities between Add-SS and SSTDM do not extend to the popular cropping attack, since when the removed area increases, performance degrades smoothly for the former but steeply for the latter. This conclusion is arrived at after comparing the analytical expressions derived for both methods. Increasing the size of the projected subspace and using a cubic lattice with repetition coding is shown to be a possible solution to cope with cropping while retaining host interference cancellation.

Security of quantization index modulation (QIM) watermarking methods is usually sought through a pseudorandom dither signal which randomizes the codebook. This dither plays the role of the secret key (i.e., a parameter only shared by the watermarking embedder and decoder), which prevents unauthorized embedding and/or decoding. However, if the same dither signal is reused, the observation of several watermarked signals can provide sufficient information for an attacker to estimate the dither signal. This paper focuses on the cases when the embedded messages are either known or constant. In the first part of this paper, a theoretical security analysis of QIM data hiding measures the information leakage about the secret dither as the mutual information between the dither and the watermarked signals. In the second part, we show how set-membership estimation techniques successfully provide accurate estimates of the dither from observed watermarked signals. The conclusion of this twofold study is that current QIM watermarking schemes have a relative low security level against this scenario because a small number of observed watermarked signals yields a sufficiently accurate estimate of the secret dither. The analysis presented in this paper also serves as the basis for more involved scenarios

In this paper, we analyze spread-spectrum and quantization projection data hiding methods from a game-theoretic point of view, using the bit error rate (BER) as the payoff, and assuming that the embedder simply follows point-by-point constraints given by a perceptual mask, whereas for the attacker an MSE-like constraint is imposed. The optimal attacking and decoding strategies are obtained by making use of a theorem that in addition states that those strategies constitute an equilibrium of the game. Experimental results supporting our analyses are also shown.

The binary distortion compensated dither-modulation (DC-DM), which can be regarded to as a baseline for quantization-based data-hiding methods, is rigorously analyzed. A novel and accurate procedure for computing the exact probability of bit error is given, as well as an approximation amenable to differentiation which allows to obtain the optimal weights in a newly proposed decoding structure, for significant improvements on performance. The results are particularized for a JPEG compression scenario which allows to show their usefulness. Experimental results validating the proposed theory are presented.