A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics
| Autor: | Ivan Castillo Camacho, Kai Wang |
|---|---|
| Přispěvatelé: | GIPSA - Apprentissage, Classification, Traitement d'Images et de Vidéos (GIPSA-ACTIV), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Cover (telecommunications)
fake image detection Computer science neural network 0211 other engineering and technologies ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image forensics Review 02 engineering and technology lcsh:Computer applications to medicine. Medical informatics lcsh:QA75.5-76.95 Image (mathematics) 0202 electrical engineering electronic engineering information engineering Radiology Nuclear Medicine and imaging lcsh:Photography Electrical and Electronic Engineering 021110 strategic defence & security studies Focus (computing) Specialized knowledge business.industry Deep learning image forensics deepfake [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM] deep learning lcsh:TR1-1050 Computer Graphics and Computer-Aided Design Data science Range (mathematics) lcsh:R858-859.7 020201 artificial intelligence & image processing Camera identification Computer Vision and Pattern Recognition Artificial intelligence lcsh:Electronic computers. Computer science business |
| Zdroj: | Journal of Imaging, Vol 7, Iss 69, p 69 (2021) Journal of Imaging Journal of Imaging, MDPI, 2021, Special Issue Image and Video Forensics, 7 (4), pp.69:1-39. ⟨10.3390/jimaging7040069⟩ |
| ISSN: | 2313-433X |
| Popis: | International audience; Seeing is not believing anymore. Different techniques have brought to our fingertips the ability to modify an image. As the difficulty of using such techniques decreases, lowering the necessity of specialized knowledge has been the focus for companies who create and sell these tools. Furthermore, image forgeries are presently so realistic that it becomes difficult for the naked eye to differentiate between fake and real media. This can bring different problems, from misleading public opinion to the usage of doctored proof in court. For these reasons, it is important to have tools that can help us discern the truth. This paper presents a comprehensive literature review of the image forensics techniques with a special focus on deep-learning-based methods. In this review, we cover a broad range of image forensics problems including the detection of routine image manipulations, detection of intentional image falsifications, camera identification, classification of computer graphics images and detection of emerging Deepfake images. With this review it can be observed that even if image forgeries are becoming easy to create, there are several options to detect each kind of them. A review of different image databases and an overview of anti-forensic methods are also presented. Finally, we suggest some future working directions that the research community could consider to tackle in a more effective way the spread of doctored images. |
| Databáze: | OpenAIRE |
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