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220822 ||| eng |
| 020 |
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|a 9783036528069
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| 020 |
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|a 9783036528076
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| 020 |
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|a books978-3-0365-2807-6
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| 100 |
1 |
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|a Amerini, Irene
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| 245 |
0 |
0 |
|a Image and Video Forensics
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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| 300 |
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|a 1 electronic resource (424 p.)
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| 653 |
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|a facial Presentation Attack Detection (PAD)
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| 653 |
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|a deepfakes
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| 653 |
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|a forensic evidence evaluation
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| 653 |
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|a image forensics
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| 653 |
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|a forged image detection
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| 653 |
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|a multimedia content manipulation
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| 653 |
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|a photo response non-uniformity
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| 653 |
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|a deep learning
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| 653 |
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|a smartphone identification
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| 653 |
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|a video forensic
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| 653 |
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|a compression
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| 653 |
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|a audio forensics
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| 653 |
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|a facial manipulations
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| 653 |
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|a deep one-class
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| 653 |
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|a hand-crafted features
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| 653 |
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|a short-time Fourier transform (STFT)
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| 653 |
|
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|a face landmarks
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| 653 |
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|a user profile linking
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| 653 |
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|a forensics detection
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| 653 |
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|a social networks
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| 653 |
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|a camera fingerprint
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| 653 |
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|a fake image
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| 653 |
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|a Information technology industries / bicssc
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| 653 |
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|a facial recognition
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| 653 |
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|a RGB camera-based anti-spoofing methods
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| 653 |
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|a social media platform identification
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| 653 |
|
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|a digital forensics
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| 653 |
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|a Deepfake
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| 653 |
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|a convolutional neural networks
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| 653 |
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|a GAN-generated image detection
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| 653 |
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|a fake image detection
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| 653 |
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|a computer vision
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| 653 |
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|a Harris
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| 653 |
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|a camera model identification
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| 653 |
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|a anomaly detection
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| 653 |
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|a face morphing
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| 653 |
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|a deepfake detection
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| 653 |
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|a media forensics
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| 653 |
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|a cybersecurity
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| 653 |
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|a PRNU
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| 653 |
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|a videos
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| 653 |
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|a survey
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| 653 |
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|a support vector machines
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| 653 |
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|a n/a
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| 653 |
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|a performance
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| 653 |
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|a discrete fourier transform
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| 653 |
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|a multimedia forensics
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| 653 |
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|a likelihood ratio
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| 653 |
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|a source identification
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| 653 |
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|a plausibility of decisions
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| 653 |
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|a facial anti-spoofing
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| 653 |
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|a simple linear iterative clustering (SLIC)
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| 653 |
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|a social network
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| 653 |
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|a video source attribution
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| 653 |
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|a VGG
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| 653 |
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|a inter-frame forgery
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| 653 |
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|a estimation by rotational invariant techniques (ESPRIT)
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| 653 |
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|a noise level function
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| 653 |
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|a blind estimation
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| 653 |
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|a GLCM
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| 653 |
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|a video forensics
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| 653 |
|
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|a transfer learning
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| 653 |
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|a multiple signal classification (MUSIC)
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| 653 |
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|a heatmap
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| 653 |
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|a correlation
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| 653 |
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|a JPEG
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| 653 |
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|a copy-move forgery detection
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| 653 |
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|a snapchat
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| 653 |
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|a UAV videos
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| 653 |
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|a digital investigations
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| 653 |
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|a Tensor
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| 653 |
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|a forensic process model
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| 653 |
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|a deepfake
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| 653 |
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|a source camera identification
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| 653 |
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|a resolution
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| 653 |
|
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|a biometrics
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| 653 |
|
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|a digital image forensics
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| 653 |
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|a classification
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| 653 |
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|a SVD
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| 653 |
|
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|a Generative Adversarial Networks
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| 653 |
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|a DeepFake detection
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| 653 |
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|a automatic border control
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| 653 |
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|a pattern recognition
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| 653 |
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|a neural network
|
| 700 |
1 |
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|a Baldini, Gianmarco
|
| 700 |
1 |
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|a Leotta, Francesco
|
| 700 |
1 |
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|a Amerini, Irene
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
|
| 028 |
5 |
0 |
|a 10.3390/books978-3-0365-2807-6
|
| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/78721
|z DOAB: description of the publication
|
| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/4812
|7 0
|x Verlag
|3 Volltext
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| 082 |
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|a 000
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|a 780
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|a 700
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|a 600
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|a Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity.
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