The pervasive availability of the Internet, coupled with the development of increasingly powerful technologies, has led digital images to be the primary source of visual information in nowadays society. However, their reliability as a true representation of reality cannot be taken for granted, due to the affordable powerful graphics editing softwares that can easily alter the original content, leaving no visual trace of any modification on the image making them potentially dangerous. This motivates developing technological solutions able to detect media manipulations without a prior knowledge or extra information regarding the given image. At the same time, the huge amount of available data has also led to tremendous advances of data-hungry learning models, which have already demonstrated in last few years to be successful in image classification. In this work we propose a deep learning approach for tampered image classification. To our best knowledge, this the first attempt to use the deep learning paradigm in an image forensic scenario. In particular, we propose a new blind deep learning approach based on Convolutional Neural Networks (CNN) able to learn invisible discriminative artifacts from manipulated images that can be exploited to automatically discriminate between forged and authentic images. The proposed approach not only detects forged images but it can be extended to localize the tampered regions within the image. This method outperforms the state-of-the-art in terms of accuracy on CASIA TIDE v2.0 dataset. The capability of automatically crafting discriminant features can lead to surprising results. For instance, detecting image compression filters used to create the dataset. This argument is also discussed within this paper.

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.

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.

We describe a new forensic technique for distinguishing between computer generated and human faces in video. This technique identifies tiny fluctuations in the appearance of a face that result from changes in blood flow. Because these changes result from the human pulse, they are unlikely to be found in computer generated imagery. We use the absence or presence of this physiological signal to distinguish computer generated from human faces.

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.

Median filtering is a well-known non linear denoising filter often used as an harmless post-processing, sometimes also employed to affect the reliability of some forensic techniques. In this work, we present a novel counter-forensic method able to conceal the characteristic traces left by median filtering. By exploiting the knowledge of features used in existing median filtering detectors, we are able to remove the characteristic footprints via suitable random pixel modification, while keeping the quality of the counter-attacked image high. Experimental results show that the proposed method is very effective, computationally efficient and competitive with other state-of-the-art techniques.

We describe a geometric technique to detect physically implausible trajectories of objects in video sequences. This technique explicitly models the three-dimensional ballistic motion of objects in free-flight and the two-dimensional projection of the trajectory into the image plane of a static or moving camera. Deviations from this model provide evidence of manipulation. The technique assumes that the object's trajectory is substantially influenced only by gravity, that the image of the object's center of mass can be determined from the images, and requires that any camera motion can be estimated from background elements. The computational requirements of the algorithm are modest, and any detected inconsistencies can be illustrated in an intuitive, geometric fashion. We demonstrate the efficacy of this analysis on videos of our own creation and on videos obtained from video-sharing websites.

In this paper we present a new near lossless reversible watermarking algorithm using adaptive prediction for embedding. The prediction is based on directional first-order differences of pixel intensities within a suitably selected neighborhood. The proposed scheme results to be computationally efficient and allows achieving high embedding capacity while preserving a high image quality. Extensive experimental results demonstrate the effectiveness of the proposed approach.

The manipulation of text on a sign or billboard is relatively easy to do in a way that is perceptually convincing. When text is on a planar surface and imaged under perspective projection, the text undergoes a specific distortion. When text is manipulated, it is unlikely to precisely satisfy this geometric mapping. We describe a technique for detecting if text in an image obeys the expected perspective projection, deviations from which are used as evidence of tampering.

In this paper we present the application of a recently developed benchmarking tool to the comparison of watermarking algorithms. We carry out an extensive analysis to assess and compare robustness of digital image watermarking techniques by considering the perceptual quality of un-marked images in terms of Weighted PSNR. Such a benchmarking tool employs genetic algorithms, introduces a novel metric for robustness assessment based on perceptual quality measures and allows analysis and comparison of different techniques performances. Experimental results show the effectiveness of the proposed approach.

This paper presents a novel and flexible benchmarking tool based on genetic algorithms (GA) and designed to assess the robustness of any digital image watermarking system. The main idea is to evaluate robustness in terms of perceptual quality, measured by weighted peak signal-to-noise ratio. Through a stochastic approach, we optimize this quality metric, by finding the minimal degradation that needs to be introduced in a marked image in order to remove the embedded watermark. Given a set of attacks, chosen according to the considered application scenario, GA support the optimization of the parameters to be assigned to each processing operation, in order to obtain an unmarked image with perceptual quality as high as possible. Extensive experimental results demonstrate the effectiveness of the proposed evaluation tool.