Information Forensics: An Overview of the First Decade

Abstract

In recent decades, we have witnessed the evolution of information technologies from the development of VLSI technologies, to communication and networking infrastructure, to the standardization of multimedia compression and coding schemes, to effective multimedia content search and retrieval. As a result, multimedia devices and digital content have become ubiquitous. This path of technological evolution has naturally led to a critical issue that must be addressed next, namely, to ensure that content, devices, and intellectual property are being used by authorized users for legitimate purposes, and to be able to forensically prove with high confidence when otherwise. When security is compromised, intellectual rights are violated, or authenticity is forged, forensic methodologies and tools are employed to reconstruct what has happened to digital content in order to answer who has done what, when, where, and how. The goal of this paper is to provide an overview on what has been done over the last decade in the new and emerging field of information forensics regarding theories, methodologies, state-of-the-art techniques, major applications, and to provide an outlook of the future.

First Steps Toward Camera Model Identification With Convolutional Neural Networks

“…In the following we describe the proposed algorithm to solve this problem 1 . We first explain how to perform the training step needed to learn the CNN and SVMs parameters.…”

“…The overall architecture is characterized by 340, 462 parameters, learned through Stochastic Gradient Descent on batches of 128 patches. Momentum is fixed to 0.9, weights decay is set 1 Code available at https: to 7.5 · 10 −3 while the learning rate is initialized to 0.015 and halves every 10 epochs. As trained CNN model M, we select the one that provides the smallest loss on validation patches within the first 50 training epochs.…”

“…As downloading, copying, forging and redistributing digital material is becoming easier over the years, some forms of regulation and authenticity verification have become of urgent necessity. To this purpose, the forensic community has developed a wide series of tools to reverse-engineer the history of multimedia objects [1], [2].…”

Photorealistic computer-generated images are difficult to distinguish from digital photographs: a case study with professional photographers and photo-editors

“…99% of the photographs in their test set. Others have followed the same path (for an overview, see Stamm et al 2013), but, as far as we know, perfect computational solutions for differentiation are lacking. This is due to studies in computer science having to deal with a specific tension in the field: while some are especially interested in reliably distinguishing photographs from computer-generated images, others have the objective of creating photorealistic renderings for which this distinction cannot be made.…”

Median Filtering Detection using Latent Growth Modeling

“…In this regard, Ray Liu [9] account for the scheme which is highly accurate in unaltered or uncompressed images.…”