Titlebook: Adversarial Multimedia Forensics; Ehsan Nowroozi,Kassem Kallas,Alireza Jolfaei Book 2024 The Editor(s) (if applicable) and The Author(s),

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Book 2024cape of multimedia forensics and data security. This book’s content can be summarized in two main areas. The first area of this book primarily addresses techniques and methodologies related to digital image forensics. It discusses advanced techniques for image manipulation detection, including the u

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https://doi.org/10.1007/978-3-540-71119-3litudes to increase resilience, while being optimized to be indistinguishable from non-watermarked weights. Experiments show that the proposed approach is able to handle large payloads without significantly affecting the accuracy of the network, and is resistant to various types of network modifications and reuse.

FLACK

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1568-2633 chitectures that pose new serious security threats against e.This book explores various aspects of digital forensics, security and machine learning, while offering valuable insights into the ever-evolving landscape of multimedia forensics and data security. This book’s content can be summarized in t

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https://doi.org/10.1007/978-3-540-71119-3ms and anti forensic targets. We provide an overview of a number of popular anti-forensic measures that apply to media forensics, along with descriptions of every measure. We also qualitatively evaluate anti forensic measures on the conflict of interest between convenience and effectiveness of the measure.

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Anti Forensic Measures and Their Impact on Forensic Investigations,ms and anti forensic targets. We provide an overview of a number of popular anti-forensic measures that apply to media forensics, along with descriptions of every measure. We also qualitatively evaluate anti forensic measures on the conflict of interest between convenience and effectiveness of the measure.

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E. Casas,J.-P. Raymond,H. Zidani”. Detecting image forgeries is important because image manipulation tools are prevalent and make it easy to spread misinformation. Existing forgery detection methods are still challenged to accurately detect image manipulations, especially when the doctored image is of a low quality (e.g., due to r

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https://doi.org/10.1007/978-3-0348-8849-3ally, GANs can be used to improve the quality of images and videos, generate synthetic data such as fictional characters and deep fakes, and apply data adaptation. In digital forensics, GANs can be used to improve the quality of machine learning (ML) algorithms. This is because the existing ML-based

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https://doi.org/10.1007/978-3-0348-8001-5hniques. These algorithms utilize Siamese neural networks to detect and localize spliced contents by identifying inconsistencies in an image’s forensic traces. At the same time, deep learning has also enabled the researchers to develop new types of anti-forensic attack that can fool forensic algorit