An Overview on Image Forensics

By Alessandro Piva. 2013

442 citations recorded by the source corpus.

The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics. These techniques have been designed to identify the source of a digital image or to determine whether the content is authentic or modified, without the knowledge of any prior information about the image under analysis (and thus are defined as passive). All these tools work by detecting the presence, the absence, or the incongruence of some traces intrinsically tied to the digital image by the acquisition device and by any other operation after its creation. The paper has been organized by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition-based methods, coding-based methods, and editing-based schemes.

Subfields: Computer Vision and Pattern Recognition, Law

View Paper: https://www.semanticscholar.org/paper/17498446b84b211105e3291a0ca4ce6228a10de9

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Citations in the corpus

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.”
MFC Datasets: Large-Scale Benchmark Datasets for Media Forensic Challenge Evaluation (2019, methodology): “…advent of new techniques such as Generative Adversarial Networks (GANs, “deepfakes”) [1] and Computer Generated Imagery (CGI) [2], it becomes increasingly challenging for existing media forensic technologies [3]-[10] to identify the integrity, trustworthiness, and authenticity of visual content.”
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].”
RAISE: a raw images dataset for digital image forensics (2015, methodology): “…splicing, enhancement, compression), the identiﬁcation of the source device that created it (e.g., camera brand and model), or the diﬀe-rentiation between contents physically recorded by a device or generated by means of computer graphics software (see, e.g., [4, 6, 15, 17] and references therein).”
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].”
Efficient Dense-Field Copy–Move Forgery Detection (2015, background): “I. INTRODUCTION IN THE last few years, a large number of techniqueshave been proposed for the detection and localization of digital image forgeries [1].”