Information Forensics: An Overview of the First Decade

By Matthew Christopher Stamm, Min Wu, K.J.R. Liu. IEEE Access, 2013

378 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/3e81e7c5e6f4caa029b6918d23b8f19f9f1f9231

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Citations in the corpus

Noiseprint: A CNN-Based Camera Model Fingerprint (2018, background): “Driven by these phenomena, in the last decade, a large number of methods have been proposed for forgery detection and localization or camera identification [1], [2], [3].”
A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer (2016, background): “variety of information forensic techniques to determine the authenticity and processing history of digital images [20].”
Media Forensics and DeepFakes: An Overview (2020, background): “There have been several reviews on this topic [11], [12], [6], [13], [14], however these last years have witnessed the advent of new methods.”
DeepFake Detection by Analyzing Convolutional Traces (2020, background): “However, the problem of authenticating an image (or specifically a digital image) is not new [2, 31, 38].”
Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection (2018, background): “To determine the authenticity and processing history of digital images, researchers have developed numerous forensic approaches during the last decade [1].”
Understanding generative AI output with embedding models (2025, unknown): “Other sources of variation are difficult to control, like effects of image watermarking and differences in camera hardware ( 43 ).”