Image and Video Forensics

Edited by
January 2022
424 pages
  • ISBN978-3-0365-2806-9 (Hardback)
  • ISBN978-3-0365-2807-6 (PDF)

This book is a reprint of the Special Issue Image and Video Forensics that was published in

Computer Science & Mathematics
Physical Sciences

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.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
face morphing; forensics detection; face landmarks; automatic border control; biometrics; facial recognition; facial anti-spoofing; facial Presentation Attack Detection (PAD); RGB camera-based anti-spoofing methods; deep learning; survey; computer vision; pattern recognition; PRNU; photo response non-uniformity; source camera identification; videos; compression; snapchat; resolution; camera fingerprint; smartphone identification; user profile linking; digital investigations; social network; classification; inter-frame forgery; digital forensics; correlation; SVD; Harris; GLCM; Tensor; video forensic; digital image forensics; source identification; GAN-generated image detection; copy-move forgery detection; computer vision; deep learning; fake image; transfer learning; VGG; image forensics; fake image detection; deep learning; neural network; Deepfake; anomaly detection; UAV videos; deep one-class; digital forensics; cybersecurity; multimedia content manipulation; deepfake; convolutional neural networks; support vector machines; discrete fourier transform; DeepFake detection; hand-crafted features; forensic process model; plausibility of decisions; forensic evidence evaluation; video source attribution; likelihood ratio; performance; blind estimation; forged image detection; heatmap; JPEG; noise level function; deepfake detection; Generative Adversarial Networks; multimedia forensics; image forensics; camera model identification; video forensics; audio forensics; convolutional neural networks; media forensics; social media platform identification; video forensics; deepfakes; video forensics; facial manipulations; social networks; deep learning; estimation by rotational invariant techniques (ESPRIT); short-time Fourier transform (STFT); multiple signal classification (MUSIC); simple linear iterative clustering (SLIC); video forensics; n/a