Visible to the public Deep Learning in Multimedia Forensics

TitleDeep Learning in Multimedia Forensics
Publication TypeConference Paper
Year of Publication2018
AuthorsVerdoliva, Luisa
Conference NameProceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5625-1
Keywordsconvolutional neural networks, Deep Learning, deep video, forgery detection and localization, Metrics, multimedia forensics, pubcrawl, Resiliency, Scalability
AbstractWith the widespread diffusion of powerful media editing tools, falsifying images and videos has become easier and easier in the last few years. Fake multimedia, often used to support fake news, represents a growing menace in many fields of life, notably in politics, journalism, and the judiciary. In response to this threat, the signal processing community has produced a major research effort. A large number of methods have been proposed for source identification, forgery detection and localization, relying on the typical signal processing tools. The advent of deep learning, however, is changing the rules of the game. On one hand, new sophisticated methods based on deep learning have been proposed to accomplish manipulations that were previously unthinkable. On the other hand, deep learning provides also the analyst with new powerful forensic tools. Given a suitably large training set, deep learning architectures ensure usually a significant performance gain with respect to conventional methods, and a much higher robustness to post-processing and evasions. In this talk after reviewing the main approaches proposed in the literature to ensure media authenticity, the most promising solutions relying on Convolutional Neural Networks will be explored with special attention to realistic scenarios, such as when manipulated images and videos are spread out over social networks. In addition, an analysis of the efficacy of adversarial attacks on such methods will be presented.
URLhttp://doi.acm.org/10.1145/3206004.3206024
DOI10.1145/3206004.3206024
Citation Keyverdoliva_deep_2018