Cecilia Pasquini

RAISE: a raw images dataset for digital image forensics

Digital forensics is a relatively new research area which aims at authenticating digital media by detecting possible digital forgeries. Indeed, the ever increasing availability of multimedia data on the web, coupled with the great advances reached by computer graphical tools, makes the modification of an image and the creation of visually compelling forgeries an easy task for any user. This in turns creates the need of reliable tools to validate the trustworthiness of the represented information. In such a context, we present here RAISE, a large dataset of 8156 high-resolution raw images, depicting various subjects and scenarios, properly annotated and available together with accompanying metadata. Such a wide collection of untouched and diverse data is intended to become a powerful resource for, but not limited to, forensic researchers by providing a common benchmark for a fair comparison, testing and evaluation of existing and next generation forensic algorithms. In this paper we describe how RAISE has been collected and organized, discuss how digital image forensics and many other multimedia research areas may benefit of this new publicly available benchmark dataset and test a very recent forensic technique for JPEG compression detection.

JPEG compression anti-forensics based on first significant digit distribution

Traces left by lossy compression processes have been widely studied in digital image forensics. In particular, the artifacts produced by JPEG compression have been characterized and exploited both in forensic methods and counter-forensic attacks. In this paper, we propose a novel anti-forensic procedure, aimed at concealing the traces of single JPEG compression by recovering the original distribution of first significant digits (FSD) of the DCT coefficients. We analyze the performance of our method and compare it with anti-forensic attacks reported in the literature in terms of quality of the resulting image. In addition, we prove the effectiveness of our approach as counter-forensic processing by measuring its impact on the performance of two different forensic tools, applied after the anti-forensic action.

A Benford-Fourier JPEG compression detector

Intrinsic statistical properties of natural uncompressed images can be used in image forensics for detecting traces of previous processing operations. In this paper, we extend the recent theoretical analysis of Benford-Fourier coefficients and propose a novel forensic detector of JPEG compression traces in images stored in an uncompressed format. The classification is based on a binary hypothesis test for which we can derive theoretically the confidence intervals, thus avoiding any training phase. Experiments on real images and comparisons with state-of-art techniques show that the proposed detector outperforms existing ones and overcomes issues due to dataset-dependency.

Multiple JPEG compression detection by means of Benford-Fourier coefficients

The analysis of JPEG compressed images is one of the most studied problems in image forensics, because of the extensive use and the characteristic traces left by such coding operation. In this paper, we propose a novel statistical framework for the identification of previous multiple aligned compressions in JPEG images and the estimation of the quality factors applied. The method has been tested on different datasets and forensic scenarios, where up to three JPEG compressions are considered. Moreover, both in the case of double and triple JPEG encoding with different quality factors, the compression history of each image is estimated. The experiments show good performance and, in most cases, higher accuracies with respect to state-of-the-art methods.

Transportation-theoretic image counterforensics to First Significant Digit histogram forensics

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.

Towards the verification of image integrity in online news

The widespread of social networking services allows users to share and quickly spread an enormous amount of digital contents. Currently, a low level of security and trustworthiness is applied to such information, whose reliability cannot be taken for granted due to the large availability of image editing software which allow any user to easily manipulate digital contents. This has a huge impact on the deception of users, whose opinion can be seriously influenced by altered media. In this work, we face the challenge of verifying online news by analyzing the images related to the particular news article. Our goal is to create an empirical system which helps in verifying the consistency of visually and semantically similar images used within different news articles on the same topic. Given a certain news online, our system identifies a set of images connected to the same topic and presenting common visual elements, which can be successively compared with the original ones and analyzed in order to discover possible inconsistencies also by means of multimedia forensics tools.

Decoy Password Vaults: At Least as Hard as Steganography?

Cracking-resistant password vaults have been recently proposed with the goal of thwarting offline attacks. This requires the generation of synthetic password vaults that are statistically indistinguishable from real ones. In this work, we establish a conceptual link between this problem and steganography, where the stego objects must be undetectable among cover objects. We compare the two frameworks and highlight parallels and differences. Moreover, we transfer results obtained in the steganography literature into the context of decoy generation. Our results include the infeasibility of perfectly secure decoy vaults and the conjecture that secure decoy vaults are at least as hard to construct as secure steganography.

Statistical Detection of JPEG Traces in Digital Images in Uncompressed Formats

Intrinsic statistical properties of natural uncompressed images are used in image forensics for detecting the traces of previous processing operations. In this paper, we propose novel forensic detectors of JPEG compression traces in images stored in uncompressed formats, based on a theoretical analysis of Benford-Fourier coefficients computed on the

Forensics of High Quality and Nearly Identical JPEG Image Recompression

8\times 8

A Deterministic Approach to Detect Median Filtering in 1D Data

block-Discrete Cosine Transform (DCT) domain. In fact, the distribution of such coefficients is derived theoretically both under the hypotheses of no compression and previous compression with a certain quality factor, allowing for the computation of the respective likelihood functions. Then, two classification tests based on different statistics are proposed, both relying on a discriminative threshold that can be determined without the need of any training phase. The statistical analysis is based on the only assumptions of generalized Gaussian distribution of DCT coefficients and independence among DCT frequencies, thus resulting in robust detectors applying to any uncompressed image. In fact, experiments on different datasets show that the proposed models are suitable for the images of different sizes and source cameras, thus overcoming dataset-dependence issues that typically affect the state-of-art techniques.