The advent of deep learning has brought a significant improvement in the quality of generated media. However, with the increased level of photorealism, synthetic media are becoming hardly distinguishable from real ones, raising serious concerns about the spread of fake or manipulated information over the Internet. In this context, it is important to develop automated tools to reliably and timely detect synthetic media. In this work, we analyze the state-of-the-art methods for the detection of synthetic images, highlighting the key ingredients of the most successful approaches, and comparing their performance over existing generative architectures. We will devote special attention to realistic and challenging scenarios, like media uploaded on social networks or generated by new and unseen architectures, analyzing the impact of suitable augmentation and training strategies on the detectors’ generalization ability.

Abstract PRNU-based image processing is a key asset in digital multimedia forensics. It allows for reliable device identification and effective detection and localization of image forgeries, in very general conditions. However, performance impairs significantly in challenging conditions involving low quality and quantity of data. These include working on compressed and cropped images or estimating the camera PRNU pattern based on only a few images. To boost the performance of PRNU-based analyses in such conditions, we propose to leverage the image noiseprint, a recently proposed camera-model fingerprint that has proved effective for several forensic tasks. Numerical experiments on datasets widely used for source identification prove that the proposed method ensures a significant performance improvement in a wide range of challenging situations.

We address the problem of clustering a set of images, according to their source device, in the absence of any prior information. Image similarity is computed based on noise residuals, regarded as single-image estimates of the camera's photo-response non-uniformity (PRNU) pattern. First, residuals are grouped by correlation clustering, and several alternative data partitions are computed as a function of a running decision boundary. Then, these partitions are processed jointly to extract a single, more reliable, consensus clustering and, with it, more reliable PRNU estimates. Finally, both clustering and PRNU estimates are progressively refined by merging pairs of the same-PRNU clusters, selected on the basis of a maximum-likelihood ratio statistic. Extensive experiments prove the proposed method to outperform the current state of the art both on pristine images and compressed images downloaded from social networks. A remarkable feature of the method is that it does not require the user to set any parameter, nor to provide a training set to estimate them. Moreover, through a suitable choice of basic tools, and efficient implementation, complexity remains always quite limited.

Digital camera identification is a very active research area, with important applications in the forensics field. Several approaches have been proposed in recent years for this task. One of the most promising is based on the estimation of the sensor noise pattern, used as a sort of camera fingerprint. However, a clever attacker can estimate a camera fingerprint and use it maliciously: this calls for new countermeasures, and so on, in a typical two-party game. In this paper we consider the triangle test, a well-know countermeasure against fake fingerprint attacks, and propose a new algorithm for improving the attacker's success rate. Numerical experiments show that, in typical scenarios, the proposed algorithm improves significantly the attacker performance.