Statistical and deterministic approaches for multimedia forensics

The increasing availability and pervasiveness of multimedia data in our society is before our very eyes. As a result of globalization and worldwide connectivity, people from all over the planet are exchanging constantly increasing amounts of images, videos, audio recordings on a daily basis. Coupled with the easy access to user-friendly editing software, this poses a number of problems related to the reliability and trustworthiness of such content, as well as its potential malevolent use. For this reason, the research field of multimedia forensics focuses on the development of forensic tools for verifying the authenticity of multimedia data. The hypothesis of pristine status of images, videos or audio tracks is called into question and can be rejected if traces of manipulation are detected with a certain degree of confidence. In this framework, studying traces left by any operation that could have been employed to process the data, either for malicious purposes or simply to improve their content or presentation, turns out to be of interest for a comprehensive forensic analysis. The goal of this doctoral study is to propose contributions to the forensic task of detecting certain processing operations in multimedia data, with particular attention to digital images. The general rationale behind our work is to leverage statistical and deterministic properties that are common to the kind of data under investigation. As it is shown in our results, this helps the forensic analysis in several directions, like assessing the confidence of a certain decision or avoiding preliminary training phases by exploiting closed-form theoretical derivations. With this respect, much work has been devoted to the study of JPEG compression traces in digital images, resulting in the development of several innovative approaches. Moreover, we have explored the possibility of defining and exploiting deterministic properties related to a certain processing operation in the forensic analysis, presenting a first approach targeted to the detection in one-dimensional data of a common data smoothing operation, the median filter.